eReefs Xarray interactiveΒΆ
import os
import numpy as np
import xarray as xr
import cartopy
import cartopy.crs as ccrs
import cartopy.feature as cfeature
from cartopy.mpl.gridliner import LONGITUDE_FORMATTER, LATITUDE_FORMATTER
cartopy.config['data_dir'] = os.getenv('CARTOPY_DIR', cartopy.config.get('data_dir'))
import cmocean
import holoviews as hv
from holoviews import opts, dim
import geoviews as gv
import geoviews.feature as gf
from cartopy import crs
gv.extension('bokeh')
import warnings
warnings.filterwarnings("ignore", category=RuntimeWarning)
year = 2018
# GBR4
base_url = "http://thredds.ereefs.aims.gov.au/thredds/dodsC/s3://aims-ereefs-public-prod/derived/ncaggregate/ereefs/GBR4_H2p0_B3p1_Cq3b_Dhnd/daily-monthly/EREEFS_AIMS-CSIRO_GBR4_H2p0_B3p1_Cq3b_Dhnd_bgc_daily-monthly-"
biofiles = [f"{base_url}{year}-{month:02}.nc" for month in range(1, 2)]
biofiles
['http://thredds.ereefs.aims.gov.au/thredds/dodsC/s3://aims-ereefs-public-prod/derived/ncaggregate/ereefs/GBR4_H2p0_B3p1_Cq3b_Dhnd/daily-monthly/EREEFS_AIMS-CSIRO_GBR4_H2p0_B3p1_Cq3b_Dhnd_bgc_daily-monthly-2018-01.nc']
ds_bio = xr.open_mfdataset(biofiles)
ds_bio
<xarray.Dataset>
Dimensions: (k: 17, latitude: 723, longitude: 491, time: 31)
Coordinates:
zc (k) float64 dask.array<chunksize=(17,), meta=np.ndarray>
* time (time) datetime64[ns] 2018-01-01T02:00:00 ... 2018-01-31...
* latitude (latitude) float64 -28.7 -28.67 -28.64 ... -7.066 -7.036
* longitude (longitude) float64 142.2 142.2 142.2 ... 156.8 156.8 156.9
Dimensions without coordinates: k
Data variables: (12/101)
alk (time, k, latitude, longitude) float32 dask.array<chunksize=(31, 17, 723, 491), meta=np.ndarray>
BOD (time, k, latitude, longitude) float32 dask.array<chunksize=(31, 17, 723, 491), meta=np.ndarray>
Chl_a_sum (time, k, latitude, longitude) float32 dask.array<chunksize=(31, 17, 723, 491), meta=np.ndarray>
CO32 (time, k, latitude, longitude) float32 dask.array<chunksize=(31, 17, 723, 491), meta=np.ndarray>
DIC (time, k, latitude, longitude) float32 dask.array<chunksize=(31, 17, 723, 491), meta=np.ndarray>
DIN (time, k, latitude, longitude) float32 dask.array<chunksize=(31, 17, 723, 491), meta=np.ndarray>
... ...
SGH_N (time, latitude, longitude) float32 dask.array<chunksize=(31, 723, 491), meta=np.ndarray>
SGH_N_pr (time, latitude, longitude) float32 dask.array<chunksize=(31, 723, 491), meta=np.ndarray>
SGHROOT_N (time, latitude, longitude) float32 dask.array<chunksize=(31, 723, 491), meta=np.ndarray>
SGROOT_N (time, latitude, longitude) float32 dask.array<chunksize=(31, 723, 491), meta=np.ndarray>
TSSM (time, latitude, longitude) float32 dask.array<chunksize=(31, 723, 491), meta=np.ndarray>
Zenith2D (time, latitude, longitude) float32 dask.array<chunksize=(31, 723, 491), meta=np.ndarray>
Attributes: (12/20)
Conventions: CF-1.0
NCO: netCDF Operators version 4.7.7 (Homepage...
RunID: 2
_CoordSysBuilder: ucar.nc2.dataset.conv.CF1Convention
aims_ncaggregate_buildDate: 2020-08-21T23:07:30+10:00
aims_ncaggregate_datasetId: products__ncaggregate__ereefs__GBR4_H2p0...
... ...
paramfile: /home/bai155/EMS_solar2/gbr4_H2p0_B3p1_C...
paramhead: eReefs 4 km grid. SOURCE Catchments with...
technical_guide_link: https://eatlas.org.au/pydio/public/aims-...
technical_guide_publish_date: 2020-08-18
title: eReefs AIMS-CSIRO GBR4 BioGeoChemical 3....
DODS_EXTRA.Unlimited_Dimension: timexarray.Dataset
- k: 17
- latitude: 723
- longitude: 491
- time: 31
- zc(k)float64dask.array<chunksize=(17,), meta=np.ndarray>
- long_name :
- Z coordinate
- _CoordinateAxisType :
- Height
- _CoordinateZisPositive :
- up
- units :
- m
- positive :
- up
- axis :
- Z
- coordinate_type :
- Z
Array Chunk Bytes 136 B 136 B Shape (17,) (17,) Count 2 Tasks 1 Chunks Type float64 numpy.ndarray - time(time)datetime64[ns]2018-01-01T02:00:00 ... 2018-01-...
- long_name :
- Time
- standard_name :
- time
- coordinate_type :
- time
- _CoordinateAxisType :
- Time
- _ChunkSizes :
- 1024
array(['2018-01-01T02:00:00.000000000', '2018-01-02T02:00:00.000000000', '2018-01-03T02:00:00.000000000', '2018-01-04T02:00:00.000000000', '2018-01-05T02:00:00.000000000', '2018-01-06T02:00:00.000000000', '2018-01-07T02:00:00.000000000', '2018-01-08T02:00:00.000000000', '2018-01-09T02:00:00.000000000', '2018-01-10T02:00:00.000000000', '2018-01-11T02:00:00.000000000', '2018-01-12T02:00:00.000000000', '2018-01-13T02:00:00.000000000', '2018-01-14T02:00:00.000000000', '2018-01-15T02:00:00.000000000', '2018-01-16T02:00:00.000000000', '2018-01-17T02:00:00.000000000', '2018-01-18T02:00:00.000000000', '2018-01-19T02:00:00.000000000', '2018-01-20T02:00:00.000000000', '2018-01-21T02:00:00.000000000', '2018-01-22T02:00:00.000000000', '2018-01-23T02:00:00.000000000', '2018-01-24T02:00:00.000000000', '2018-01-25T02:00:00.000000000', '2018-01-26T02:00:00.000000000', '2018-01-27T02:00:00.000000000', '2018-01-28T02:00:00.000000000', '2018-01-29T02:00:00.000000000', '2018-01-30T02:00:00.000000000', '2018-01-31T02:00:00.000000000'], dtype='datetime64[ns]') - latitude(latitude)float64-28.7 -28.67 ... -7.066 -7.036
- units :
- degrees_north
- long_name :
- Latitude
- standard_name :
- latitude
- projection :
- geographic
- coordinate_type :
- latitude
- _CoordinateAxisType :
- Lat
array([-28.696022, -28.666022, -28.636022, ..., -7.096022, -7.066022, -7.036022]) - longitude(longitude)float64142.2 142.2 142.2 ... 156.8 156.9
- units :
- degrees_east
- long_name :
- Longitude
- standard_name :
- longitude
- projection :
- geographic
- coordinate_type :
- longitude
- _CoordinateAxisType :
- Lon
array([142.168788, 142.198788, 142.228788, ..., 156.808788, 156.838788, 156.868788])
- alk(time, k, latitude, longitude)float32dask.array<chunksize=(31, 17, 723, 491), meta=np.ndarray>
- short_name :
- alk
- units :
- mmol m-3
- long_name :
- Total alkalinity
- _ChunkSizes :
- [ 1 1 133 491]
Array Chunk Bytes 748.33 MB 748.33 MB Shape (31, 17, 723, 491) (31, 17, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - BOD(time, k, latitude, longitude)float32dask.array<chunksize=(31, 17, 723, 491), meta=np.ndarray>
- short_name :
- BOD
- units :
- mg O m-3
- long_name :
- Biochemical Oxygen Demand
- _ChunkSizes :
- [ 1 1 133 491]
Array Chunk Bytes 748.33 MB 748.33 MB Shape (31, 17, 723, 491) (31, 17, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - Chl_a_sum(time, k, latitude, longitude)float32dask.array<chunksize=(31, 17, 723, 491), meta=np.ndarray>
- short_name :
- Chl_a_sum
- units :
- mg Chl m-3
- long_name :
- Total Chlorophyll
- _ChunkSizes :
- [ 1 1 133 491]
Array Chunk Bytes 748.33 MB 748.33 MB Shape (31, 17, 723, 491) (31, 17, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - CO32(time, k, latitude, longitude)float32dask.array<chunksize=(31, 17, 723, 491), meta=np.ndarray>
- short_name :
- CO32
- units :
- mmol m-3
- long_name :
- Carbonate
- _ChunkSizes :
- [ 1 1 133 491]
Array Chunk Bytes 748.33 MB 748.33 MB Shape (31, 17, 723, 491) (31, 17, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - DIC(time, k, latitude, longitude)float32dask.array<chunksize=(31, 17, 723, 491), meta=np.ndarray>
- short_name :
- DIC
- units :
- mg C m-3
- long_name :
- Dissolved Inorganic Carbon
- _ChunkSizes :
- [ 1 1 133 491]
Array Chunk Bytes 748.33 MB 748.33 MB Shape (31, 17, 723, 491) (31, 17, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - DIN(time, k, latitude, longitude)float32dask.array<chunksize=(31, 17, 723, 491), meta=np.ndarray>
- short_name :
- DIN
- units :
- mg N m-3
- long_name :
- Dissolved Inorganic Nitrogen
- _ChunkSizes :
- [ 1 1 133 491]
Array Chunk Bytes 748.33 MB 748.33 MB Shape (31, 17, 723, 491) (31, 17, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - DIP(time, k, latitude, longitude)float32dask.array<chunksize=(31, 17, 723, 491), meta=np.ndarray>
- short_name :
- DIP
- units :
- mg P m-3
- long_name :
- Dissolved Inorganic Phosphorus
- _ChunkSizes :
- [ 1 1 133 491]
Array Chunk Bytes 748.33 MB 748.33 MB Shape (31, 17, 723, 491) (31, 17, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - DOR_C(time, k, latitude, longitude)float32dask.array<chunksize=(31, 17, 723, 491), meta=np.ndarray>
- short_name :
- DOR_C
- units :
- mg C m-3
- long_name :
- Dissolved Organic Carbon
- _ChunkSizes :
- [ 1 1 133 491]
Array Chunk Bytes 748.33 MB 748.33 MB Shape (31, 17, 723, 491) (31, 17, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - DOR_N(time, k, latitude, longitude)float32dask.array<chunksize=(31, 17, 723, 491), meta=np.ndarray>
- short_name :
- DOR_N
- units :
- mg N m-3
- long_name :
- Dissolved Organic Nitrogen
- _ChunkSizes :
- [ 1 1 133 491]
Array Chunk Bytes 748.33 MB 748.33 MB Shape (31, 17, 723, 491) (31, 17, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - DOR_P(time, k, latitude, longitude)float32dask.array<chunksize=(31, 17, 723, 491), meta=np.ndarray>
- short_name :
- DOR_P
- units :
- mg P m-3
- long_name :
- Dissolved Organic Phosphorus
- _ChunkSizes :
- [ 1 1 133 491]
Array Chunk Bytes 748.33 MB 748.33 MB Shape (31, 17, 723, 491) (31, 17, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - Dust(time, k, latitude, longitude)float32dask.array<chunksize=(31, 17, 723, 491), meta=np.ndarray>
- short_name :
- Dust
- units :
- kg m-3
- long_name :
- Dust
- _ChunkSizes :
- [ 1 1 133 491]
Array Chunk Bytes 748.33 MB 748.33 MB Shape (31, 17, 723, 491) (31, 17, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - EFI(time, k, latitude, longitude)float32dask.array<chunksize=(31, 17, 723, 491), meta=np.ndarray>
- short_name :
- EFI
- units :
- kg m-3
- long_name :
- Ecology Fine Inorganics
- _ChunkSizes :
- [ 1 1 133 491]
Array Chunk Bytes 748.33 MB 748.33 MB Shape (31, 17, 723, 491) (31, 17, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - FineSed(time, k, latitude, longitude)float32dask.array<chunksize=(31, 17, 723, 491), meta=np.ndarray>
- short_name :
- FineSed
- units :
- kg m-3
- long_name :
- FineSed
- _ChunkSizes :
- [ 1 1 133 491]
Array Chunk Bytes 748.33 MB 748.33 MB Shape (31, 17, 723, 491) (31, 17, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - Fluorescence(time, k, latitude, longitude)float32dask.array<chunksize=(31, 17, 723, 491), meta=np.ndarray>
- short_name :
- Fluorescence
- units :
- mg chla m-3
- long_name :
- Simulated Fluorescence
- _ChunkSizes :
- [ 1 1 133 491]
Array Chunk Bytes 748.33 MB 748.33 MB Shape (31, 17, 723, 491) (31, 17, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - HCO3(time, k, latitude, longitude)float32dask.array<chunksize=(31, 17, 723, 491), meta=np.ndarray>
- short_name :
- HCO3
- units :
- mmol m-3
- long_name :
- Bicarbonate
- _ChunkSizes :
- [ 1 1 133 491]
Array Chunk Bytes 748.33 MB 748.33 MB Shape (31, 17, 723, 491) (31, 17, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - Kd_490(time, k, latitude, longitude)float32dask.array<chunksize=(31, 17, 723, 491), meta=np.ndarray>
- short_name :
- Kd_490
- units :
- m-1
- long_name :
- Vert. att. at 490 nm
- _ChunkSizes :
- [ 1 1 133 491]
Array Chunk Bytes 748.33 MB 748.33 MB Shape (31, 17, 723, 491) (31, 17, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - MPB_Chl(time, k, latitude, longitude)float32dask.array<chunksize=(31, 17, 723, 491), meta=np.ndarray>
- short_name :
- MPB_Chl
- units :
- mg Chl m-3
- long_name :
- Microphytobenthos chlorophyll
- _ChunkSizes :
- [ 1 1 133 491]
Array Chunk Bytes 748.33 MB 748.33 MB Shape (31, 17, 723, 491) (31, 17, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - MPB_N(time, k, latitude, longitude)float32dask.array<chunksize=(31, 17, 723, 491), meta=np.ndarray>
- short_name :
- MPB_N
- units :
- mg N m-3
- long_name :
- Microphytobenthos N
- _ChunkSizes :
- [ 1 1 133 491]
Array Chunk Bytes 748.33 MB 748.33 MB Shape (31, 17, 723, 491) (31, 17, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - Mud-carbonate(time, k, latitude, longitude)float32dask.array<chunksize=(31, 17, 723, 491), meta=np.ndarray>
- short_name :
- Mud-carbonate
- units :
- kg m-3
- long_name :
- Mud-carbonate
- _ChunkSizes :
- [ 1 1 133 491]
Array Chunk Bytes 748.33 MB 748.33 MB Shape (31, 17, 723, 491) (31, 17, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - Mud-mineral(time, k, latitude, longitude)float32dask.array<chunksize=(31, 17, 723, 491), meta=np.ndarray>
- short_name :
- Mud-mineral
- units :
- kg m-3
- long_name :
- Mud-mineral
- _ChunkSizes :
- [ 1 1 133 491]
Array Chunk Bytes 748.33 MB 748.33 MB Shape (31, 17, 723, 491) (31, 17, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - Nfix(time, k, latitude, longitude)float32dask.array<chunksize=(31, 17, 723, 491), meta=np.ndarray>
- short_name :
- Nfix
- units :
- mg N m-3 s-1
- long_name :
- N2 fixation
- _ChunkSizes :
- [ 1 1 133 491]
Array Chunk Bytes 748.33 MB 748.33 MB Shape (31, 17, 723, 491) (31, 17, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - NH4(time, k, latitude, longitude)float32dask.array<chunksize=(31, 17, 723, 491), meta=np.ndarray>
- short_name :
- NH4
- units :
- mg N m-3
- long_name :
- Ammonia
- _ChunkSizes :
- [ 1 1 133 491]
Array Chunk Bytes 748.33 MB 748.33 MB Shape (31, 17, 723, 491) (31, 17, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - NO3(time, k, latitude, longitude)float32dask.array<chunksize=(31, 17, 723, 491), meta=np.ndarray>
- short_name :
- NO3
- units :
- mg N m-3
- long_name :
- Nitrate
- _ChunkSizes :
- [ 1 1 133 491]
Array Chunk Bytes 748.33 MB 748.33 MB Shape (31, 17, 723, 491) (31, 17, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - omega_ar(time, k, latitude, longitude)float32dask.array<chunksize=(31, 17, 723, 491), meta=np.ndarray>
- short_name :
- omega_ar
- units :
- nil
- long_name :
- Aragonite saturation state
- _ChunkSizes :
- [ 1 1 133 491]
Array Chunk Bytes 748.33 MB 748.33 MB Shape (31, 17, 723, 491) (31, 17, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - Oxy_sat(time, k, latitude, longitude)float32dask.array<chunksize=(31, 17, 723, 491), meta=np.ndarray>
- short_name :
- Oxy_sat
- units :
- %
- long_name :
- Oxygen saturation percent
- _ChunkSizes :
- [ 1 1 133 491]
Array Chunk Bytes 748.33 MB 748.33 MB Shape (31, 17, 723, 491) (31, 17, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - Oxygen(time, k, latitude, longitude)float32dask.array<chunksize=(31, 17, 723, 491), meta=np.ndarray>
- short_name :
- Oxygen
- units :
- mg O m-3
- long_name :
- Dissolved Oxygen
- _ChunkSizes :
- [ 1 1 133 491]
Array Chunk Bytes 748.33 MB 748.33 MB Shape (31, 17, 723, 491) (31, 17, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - P_Prod(time, k, latitude, longitude)float32dask.array<chunksize=(31, 17, 723, 491), meta=np.ndarray>
- short_name :
- P_Prod
- units :
- mg C m-3 d-1
- long_name :
- Phytoplankton total productivity
- _ChunkSizes :
- [ 1 1 133 491]
Array Chunk Bytes 748.33 MB 748.33 MB Shape (31, 17, 723, 491) (31, 17, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - PAR(time, k, latitude, longitude)float32dask.array<chunksize=(31, 17, 723, 491), meta=np.ndarray>
- short_name :
- PAR
- units :
- mol photon m-2 s-1
- long_name :
- Av. PAR in layer
- _ChunkSizes :
- [ 1 1 133 491]
Array Chunk Bytes 748.33 MB 748.33 MB Shape (31, 17, 723, 491) (31, 17, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - PAR_z(time, k, latitude, longitude)float32dask.array<chunksize=(31, 17, 723, 491), meta=np.ndarray>
- short_name :
- PAR_z
- units :
- mol photon m-2 s-1
- long_name :
- Downwelling PAR at top of layer
- _ChunkSizes :
- [ 1 1 133 491]
Array Chunk Bytes 748.33 MB 748.33 MB Shape (31, 17, 723, 491) (31, 17, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - pco2surf(time, k, latitude, longitude)float32dask.array<chunksize=(31, 17, 723, 491), meta=np.ndarray>
- short_name :
- pco2surf
- units :
- ppmv
- long_name :
- oceanic pCO2 (ppmv)
- _ChunkSizes :
- [ 1 1 133 491]
Array Chunk Bytes 748.33 MB 748.33 MB Shape (31, 17, 723, 491) (31, 17, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - PH(time, k, latitude, longitude)float32dask.array<chunksize=(31, 17, 723, 491), meta=np.ndarray>
- short_name :
- PH
- units :
- log(mM)
- long_name :
- PH
- _ChunkSizes :
- [ 1 1 133 491]
Array Chunk Bytes 748.33 MB 748.33 MB Shape (31, 17, 723, 491) (31, 17, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - PhyL_Chl(time, k, latitude, longitude)float32dask.array<chunksize=(31, 17, 723, 491), meta=np.ndarray>
- short_name :
- PhyL_Chl
- units :
- mg Chl m-3
- long_name :
- Large Phytoplankton chlorophyll
- _ChunkSizes :
- [ 1 1 133 491]
Array Chunk Bytes 748.33 MB 748.33 MB Shape (31, 17, 723, 491) (31, 17, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - PhyL_N(time, k, latitude, longitude)float32dask.array<chunksize=(31, 17, 723, 491), meta=np.ndarray>
- short_name :
- PhyL_N
- units :
- mg N m-3
- long_name :
- Large Phytoplankton N
- _ChunkSizes :
- [ 1 1 133 491]
Array Chunk Bytes 748.33 MB 748.33 MB Shape (31, 17, 723, 491) (31, 17, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - PhyS_Chl(time, k, latitude, longitude)float32dask.array<chunksize=(31, 17, 723, 491), meta=np.ndarray>
- short_name :
- PhyS_Chl
- units :
- mg Chl m-3
- long_name :
- Small Phytoplankton chlorophyll
- _ChunkSizes :
- [ 1 1 133 491]
Array Chunk Bytes 748.33 MB 748.33 MB Shape (31, 17, 723, 491) (31, 17, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - PhyS_N(time, k, latitude, longitude)float32dask.array<chunksize=(31, 17, 723, 491), meta=np.ndarray>
- short_name :
- PhyS_N
- units :
- mg N m-3
- long_name :
- Small Phytoplankton N
- _ChunkSizes :
- [ 1 1 133 491]
Array Chunk Bytes 748.33 MB 748.33 MB Shape (31, 17, 723, 491) (31, 17, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - PhyS_NR(time, k, latitude, longitude)float32dask.array<chunksize=(31, 17, 723, 491), meta=np.ndarray>
- short_name :
- PhyS_NR
- units :
- mg N m-3
- long_name :
- Small Phytoplankton N reserve
- _ChunkSizes :
- [ 1 1 133 491]
Array Chunk Bytes 748.33 MB 748.33 MB Shape (31, 17, 723, 491) (31, 17, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - PIP(time, k, latitude, longitude)float32dask.array<chunksize=(31, 17, 723, 491), meta=np.ndarray>
- short_name :
- PIP
- units :
- mg P m-3
- long_name :
- Particulate Inorganic Phosphorus
- _ChunkSizes :
- [ 1 1 133 491]
Array Chunk Bytes 748.33 MB 748.33 MB Shape (31, 17, 723, 491) (31, 17, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - salt(time, k, latitude, longitude)float32dask.array<chunksize=(31, 17, 723, 491), meta=np.ndarray>
- short_name :
- salt
- units :
- PSU
- long_name :
- Salinity
- _ChunkSizes :
- [ 1 1 133 491]
Array Chunk Bytes 748.33 MB 748.33 MB Shape (31, 17, 723, 491) (31, 17, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - TC(time, k, latitude, longitude)float32dask.array<chunksize=(31, 17, 723, 491), meta=np.ndarray>
- short_name :
- TC
- units :
- mg C m-3
- long_name :
- Total C
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Array Chunk Bytes 748.33 MB 748.33 MB Shape (31, 17, 723, 491) (31, 17, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - temp(time, k, latitude, longitude)float32dask.array<chunksize=(31, 17, 723, 491), meta=np.ndarray>
- short_name :
- temp
- units :
- degrees C
- long_name :
- Temperature
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Array Chunk Bytes 748.33 MB 748.33 MB Shape (31, 17, 723, 491) (31, 17, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - TN(time, k, latitude, longitude)float32dask.array<chunksize=(31, 17, 723, 491), meta=np.ndarray>
- short_name :
- TN
- units :
- mg N m-3
- long_name :
- Total N
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Array Chunk Bytes 748.33 MB 748.33 MB Shape (31, 17, 723, 491) (31, 17, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - TP(time, k, latitude, longitude)float32dask.array<chunksize=(31, 17, 723, 491), meta=np.ndarray>
- short_name :
- TP
- units :
- mg P m-3
- long_name :
- Total P
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Array Chunk Bytes 748.33 MB 748.33 MB Shape (31, 17, 723, 491) (31, 17, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - Tricho_Chl(time, k, latitude, longitude)float32dask.array<chunksize=(31, 17, 723, 491), meta=np.ndarray>
- short_name :
- Tricho_Chl
- units :
- mg Chl m-3
- long_name :
- Trichodesmium chlorophyll
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Array Chunk Bytes 748.33 MB 748.33 MB Shape (31, 17, 723, 491) (31, 17, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - Tricho_N(time, k, latitude, longitude)float32dask.array<chunksize=(31, 17, 723, 491), meta=np.ndarray>
- short_name :
- Tricho_N
- units :
- mg N m-3
- long_name :
- Trichodesmium Nitrogen
- _ChunkSizes :
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Array Chunk Bytes 748.33 MB 748.33 MB Shape (31, 17, 723, 491) (31, 17, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - Z_grazing(time, k, latitude, longitude)float32dask.array<chunksize=(31, 17, 723, 491), meta=np.ndarray>
- short_name :
- Z_grazing
- units :
- mg C m-3 d-1
- long_name :
- Zooplankton total grazing
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Array Chunk Bytes 748.33 MB 748.33 MB Shape (31, 17, 723, 491) (31, 17, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - ZooL_N(time, k, latitude, longitude)float32dask.array<chunksize=(31, 17, 723, 491), meta=np.ndarray>
- short_name :
- ZooL_N
- units :
- mg N m-3
- long_name :
- Large Zooplankton N
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Array Chunk Bytes 748.33 MB 748.33 MB Shape (31, 17, 723, 491) (31, 17, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - ZooS_N(time, k, latitude, longitude)float32dask.array<chunksize=(31, 17, 723, 491), meta=np.ndarray>
- short_name :
- ZooS_N
- units :
- mg N m-3
- long_name :
- Small Zooplankton N
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Array Chunk Bytes 748.33 MB 748.33 MB Shape (31, 17, 723, 491) (31, 17, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - CH_N(time, latitude, longitude)float32dask.array<chunksize=(31, 723, 491), meta=np.ndarray>
- short_name :
- CH_N
- units :
- g N m-2
- long_name :
- Coral host N
- _ChunkSizes :
- [ 1 133 491]
Array Chunk Bytes 44.02 MB 44.02 MB Shape (31, 723, 491) (31, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - CS_bleach(time, latitude, longitude)float32dask.array<chunksize=(31, 723, 491), meta=np.ndarray>
- short_name :
- CS_bleach
- units :
- d-1
- long_name :
- Coral bleach rate
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Array Chunk Bytes 44.02 MB 44.02 MB Shape (31, 723, 491) (31, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - CS_Chl(time, latitude, longitude)float32dask.array<chunksize=(31, 723, 491), meta=np.ndarray>
- short_name :
- CS_Chl
- units :
- mg Chl m-2
- long_name :
- Coral symbiont Chl
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Array Chunk Bytes 44.02 MB 44.02 MB Shape (31, 723, 491) (31, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - CS_N(time, latitude, longitude)float32dask.array<chunksize=(31, 723, 491), meta=np.ndarray>
- short_name :
- CS_N
- units :
- mg N m-2
- long_name :
- Coral symbiont N
- _ChunkSizes :
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Array Chunk Bytes 44.02 MB 44.02 MB Shape (31, 723, 491) (31, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - EpiPAR_sg(time, latitude, longitude)float32dask.array<chunksize=(31, 723, 491), meta=np.ndarray>
- short_name :
- EpiPAR_sg
- units :
- mol photon m-2 d-1
- long_name :
- Light intensity above seagrass
- _ChunkSizes :
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Array Chunk Bytes 44.02 MB 44.02 MB Shape (31, 723, 491) (31, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - eta(time, latitude, longitude)float32dask.array<chunksize=(31, 723, 491), meta=np.ndarray>
- short_name :
- eta
- units :
- metre
- long_name :
- Surface Elevation
- _ChunkSizes :
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Array Chunk Bytes 44.02 MB 44.02 MB Shape (31, 723, 491) (31, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - MA_N(time, latitude, longitude)float32dask.array<chunksize=(31, 723, 491), meta=np.ndarray>
- short_name :
- MA_N
- units :
- g N m-2
- long_name :
- Macroalgae N
- _ChunkSizes :
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Array Chunk Bytes 44.02 MB 44.02 MB Shape (31, 723, 491) (31, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - MA_N_pr(time, latitude, longitude)float32dask.array<chunksize=(31, 723, 491), meta=np.ndarray>
- short_name :
- MA_N_pr
- units :
- mg N m-2 d-1
- long_name :
- Macroalgae net production
- _ChunkSizes :
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Array Chunk Bytes 44.02 MB 44.02 MB Shape (31, 723, 491) (31, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - month_EpiPAR_sg(time, latitude, longitude)float32dask.array<chunksize=(31, 723, 491), meta=np.ndarray>
- short_name :
- month_EpiPAR_sg
- units :
- mol photon m-2
- long_name :
- Monthly dose light above seagrass
- _ChunkSizes :
- [ 1 133 491]
Array Chunk Bytes 44.02 MB 44.02 MB Shape (31, 723, 491) (31, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - R_400(time, latitude, longitude)float32dask.array<chunksize=(31, 723, 491), meta=np.ndarray>
- short_name :
- R_400
- units :
- sr-1
- long_name :
- Remote-sensing reflectance @ 400 nm
- _ChunkSizes :
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Array Chunk Bytes 44.02 MB 44.02 MB Shape (31, 723, 491) (31, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - R_410(time, latitude, longitude)float32dask.array<chunksize=(31, 723, 491), meta=np.ndarray>
- short_name :
- R_410
- units :
- sr-1
- long_name :
- Remote-sensing reflectance @ 410 nm
- _ChunkSizes :
- [ 1 133 491]
Array Chunk Bytes 44.02 MB 44.02 MB Shape (31, 723, 491) (31, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - R_412(time, latitude, longitude)float32dask.array<chunksize=(31, 723, 491), meta=np.ndarray>
- short_name :
- R_412
- units :
- sr-1
- long_name :
- Remote-sensing reflectance @ 412 nm
- _ChunkSizes :
- [ 1 133 491]
Array Chunk Bytes 44.02 MB 44.02 MB Shape (31, 723, 491) (31, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - R_443(time, latitude, longitude)float32dask.array<chunksize=(31, 723, 491), meta=np.ndarray>
- short_name :
- R_443
- units :
- sr-1
- long_name :
- Remote-sensing reflectance @ 443 nm
- _ChunkSizes :
- [ 1 133 491]
Array Chunk Bytes 44.02 MB 44.02 MB Shape (31, 723, 491) (31, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - R_470(time, latitude, longitude)float32dask.array<chunksize=(31, 723, 491), meta=np.ndarray>
- short_name :
- R_470
- units :
- sr-1
- long_name :
- Remote-sensing reflectance @ 470 nm
- _ChunkSizes :
- [ 1 133 491]
Array Chunk Bytes 44.02 MB 44.02 MB Shape (31, 723, 491) (31, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - R_486(time, latitude, longitude)float32dask.array<chunksize=(31, 723, 491), meta=np.ndarray>
- short_name :
- R_486
- units :
- sr-1
- long_name :
- Remote-sensing reflectance @ 486 nm
- _ChunkSizes :
- [ 1 133 491]
Array Chunk Bytes 44.02 MB 44.02 MB Shape (31, 723, 491) (31, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - R_488(time, latitude, longitude)float32dask.array<chunksize=(31, 723, 491), meta=np.ndarray>
- short_name :
- R_488
- units :
- sr-1
- long_name :
- Remote-sensing reflectance @ 488 nm
- _ChunkSizes :
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Array Chunk Bytes 44.02 MB 44.02 MB Shape (31, 723, 491) (31, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - R_490(time, latitude, longitude)float32dask.array<chunksize=(31, 723, 491), meta=np.ndarray>
- short_name :
- R_490
- units :
- sr-1
- long_name :
- Remote-sensing reflectance @ 490 nm
- _ChunkSizes :
- [ 1 133 491]
Array Chunk Bytes 44.02 MB 44.02 MB Shape (31, 723, 491) (31, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - R_510(time, latitude, longitude)float32dask.array<chunksize=(31, 723, 491), meta=np.ndarray>
- short_name :
- R_510
- units :
- sr-1
- long_name :
- Remote-sensing reflectance @ 510 nm
- _ChunkSizes :
- [ 1 133 491]
Array Chunk Bytes 44.02 MB 44.02 MB Shape (31, 723, 491) (31, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - R_531(time, latitude, longitude)float32dask.array<chunksize=(31, 723, 491), meta=np.ndarray>
- short_name :
- R_531
- units :
- sr-1
- long_name :
- Remote-sensing reflectance @ 531 nm
- _ChunkSizes :
- [ 1 133 491]
Array Chunk Bytes 44.02 MB 44.02 MB Shape (31, 723, 491) (31, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - R_547(time, latitude, longitude)float32dask.array<chunksize=(31, 723, 491), meta=np.ndarray>
- short_name :
- R_547
- units :
- sr-1
- long_name :
- Remote-sensing reflectance @ 547 nm
- _ChunkSizes :
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Array Chunk Bytes 44.02 MB 44.02 MB Shape (31, 723, 491) (31, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - R_551(time, latitude, longitude)float32dask.array<chunksize=(31, 723, 491), meta=np.ndarray>
- short_name :
- R_551
- units :
- sr-1
- long_name :
- Remote-sensing reflectance @ 551 nm
- _ChunkSizes :
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Array Chunk Bytes 44.02 MB 44.02 MB Shape (31, 723, 491) (31, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - R_555(time, latitude, longitude)float32dask.array<chunksize=(31, 723, 491), meta=np.ndarray>
- short_name :
- R_555
- units :
- sr-1
- long_name :
- Remote-sensing reflectance @ 555 nm
- _ChunkSizes :
- [ 1 133 491]
Array Chunk Bytes 44.02 MB 44.02 MB Shape (31, 723, 491) (31, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - R_560(time, latitude, longitude)float32dask.array<chunksize=(31, 723, 491), meta=np.ndarray>
- short_name :
- R_560
- units :
- sr-1
- long_name :
- Remote-sensing reflectance @ 560 nm
- _ChunkSizes :
- [ 1 133 491]
Array Chunk Bytes 44.02 MB 44.02 MB Shape (31, 723, 491) (31, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - R_590(time, latitude, longitude)float32dask.array<chunksize=(31, 723, 491), meta=np.ndarray>
- short_name :
- R_590
- units :
- sr-1
- long_name :
- Remote-sensing reflectance @ 590 nm
- _ChunkSizes :
- [ 1 133 491]
Array Chunk Bytes 44.02 MB 44.02 MB Shape (31, 723, 491) (31, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - R_620(time, latitude, longitude)float32dask.array<chunksize=(31, 723, 491), meta=np.ndarray>
- short_name :
- R_620
- units :
- sr-1
- long_name :
- Remote-sensing reflectance @ 620 nm
- _ChunkSizes :
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Array Chunk Bytes 44.02 MB 44.02 MB Shape (31, 723, 491) (31, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - R_640(time, latitude, longitude)float32dask.array<chunksize=(31, 723, 491), meta=np.ndarray>
- short_name :
- R_640
- units :
- sr-1
- long_name :
- Remote-sensing reflectance @ 640 nm
- _ChunkSizes :
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Array Chunk Bytes 44.02 MB 44.02 MB Shape (31, 723, 491) (31, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - R_645(time, latitude, longitude)float32dask.array<chunksize=(31, 723, 491), meta=np.ndarray>
- short_name :
- R_645
- units :
- sr-1
- long_name :
- Remote-sensing reflectance @ 645 nm
- _ChunkSizes :
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Array Chunk Bytes 44.02 MB 44.02 MB Shape (31, 723, 491) (31, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - R_665(time, latitude, longitude)float32dask.array<chunksize=(31, 723, 491), meta=np.ndarray>
- short_name :
- R_665
- units :
- sr-1
- long_name :
- Remote-sensing reflectance @ 665 nm
- _ChunkSizes :
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Array Chunk Bytes 44.02 MB 44.02 MB Shape (31, 723, 491) (31, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - R_667(time, latitude, longitude)float32dask.array<chunksize=(31, 723, 491), meta=np.ndarray>
- short_name :
- R_667
- units :
- sr-1
- long_name :
- Remote-sensing reflectance @ 667 nm
- _ChunkSizes :
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Array Chunk Bytes 44.02 MB 44.02 MB Shape (31, 723, 491) (31, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - R_671(time, latitude, longitude)float32dask.array<chunksize=(31, 723, 491), meta=np.ndarray>
- short_name :
- R_671
- units :
- sr-1
- long_name :
- Remote-sensing reflectance @ 671 nm
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Array Chunk Bytes 44.02 MB 44.02 MB Shape (31, 723, 491) (31, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - R_673(time, latitude, longitude)float32dask.array<chunksize=(31, 723, 491), meta=np.ndarray>
- short_name :
- R_673
- units :
- sr-1
- long_name :
- Remote-sensing reflectance @ 673 nm
- _ChunkSizes :
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Array Chunk Bytes 44.02 MB 44.02 MB Shape (31, 723, 491) (31, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - R_678(time, latitude, longitude)float32dask.array<chunksize=(31, 723, 491), meta=np.ndarray>
- short_name :
- R_678
- units :
- sr-1
- long_name :
- Remote-sensing reflectance @ 678 nm
- _ChunkSizes :
- [ 1 133 491]
Array Chunk Bytes 44.02 MB 44.02 MB Shape (31, 723, 491) (31, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - R_681(time, latitude, longitude)float32dask.array<chunksize=(31, 723, 491), meta=np.ndarray>
- short_name :
- R_681
- units :
- sr-1
- long_name :
- Remote-sensing reflectance @ 681 nm
- _ChunkSizes :
- [ 1 133 491]
Array Chunk Bytes 44.02 MB 44.02 MB Shape (31, 723, 491) (31, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - R_709(time, latitude, longitude)float32dask.array<chunksize=(31, 723, 491), meta=np.ndarray>
- short_name :
- R_709
- units :
- sr-1
- long_name :
- Remote-sensing reflectance @ 709 nm
- _ChunkSizes :
- [ 1 133 491]
Array Chunk Bytes 44.02 MB 44.02 MB Shape (31, 723, 491) (31, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - R_745(time, latitude, longitude)float32dask.array<chunksize=(31, 723, 491), meta=np.ndarray>
- short_name :
- R_745
- units :
- sr-1
- long_name :
- Remote-sensing reflectance @ 745 nm
- _ChunkSizes :
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Array Chunk Bytes 44.02 MB 44.02 MB Shape (31, 723, 491) (31, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - R_748(time, latitude, longitude)float32dask.array<chunksize=(31, 723, 491), meta=np.ndarray>
- short_name :
- R_748
- units :
- sr-1
- long_name :
- Remote-sensing reflectance @ 748 nm
- _ChunkSizes :
- [ 1 133 491]
Array Chunk Bytes 44.02 MB 44.02 MB Shape (31, 723, 491) (31, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - R_754(time, latitude, longitude)float32dask.array<chunksize=(31, 723, 491), meta=np.ndarray>
- short_name :
- R_754
- units :
- sr-1
- long_name :
- Remote-sensing reflectance @ 754 nm
- _ChunkSizes :
- [ 1 133 491]
Array Chunk Bytes 44.02 MB 44.02 MB Shape (31, 723, 491) (31, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - R_761(time, latitude, longitude)float32dask.array<chunksize=(31, 723, 491), meta=np.ndarray>
- short_name :
- R_761
- units :
- sr-1
- long_name :
- Remote-sensing reflectance @ 761 nm
- _ChunkSizes :
- [ 1 133 491]
Array Chunk Bytes 44.02 MB 44.02 MB Shape (31, 723, 491) (31, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - R_764(time, latitude, longitude)float32dask.array<chunksize=(31, 723, 491), meta=np.ndarray>
- short_name :
- R_764
- units :
- sr-1
- long_name :
- Remote-sensing reflectance @ 764 nm
- _ChunkSizes :
- [ 1 133 491]
Array Chunk Bytes 44.02 MB 44.02 MB Shape (31, 723, 491) (31, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - R_767(time, latitude, longitude)float32dask.array<chunksize=(31, 723, 491), meta=np.ndarray>
- short_name :
- R_767
- units :
- sr-1
- long_name :
- Remote-sensing reflectance @ 767 nm
- _ChunkSizes :
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Array Chunk Bytes 44.02 MB 44.02 MB Shape (31, 723, 491) (31, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - R_778(time, latitude, longitude)float32dask.array<chunksize=(31, 723, 491), meta=np.ndarray>
- short_name :
- R_778
- units :
- sr-1
- long_name :
- Remote-sensing reflectance @ 778 nm
- _ChunkSizes :
- [ 1 133 491]
Array Chunk Bytes 44.02 MB 44.02 MB Shape (31, 723, 491) (31, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - Secchi(time, latitude, longitude)float32dask.array<chunksize=(31, 723, 491), meta=np.ndarray>
- short_name :
- Secchi
- units :
- m
- long_name :
- Secchi from 488 nm
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Array Chunk Bytes 44.02 MB 44.02 MB Shape (31, 723, 491) (31, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - SG_N(time, latitude, longitude)float32dask.array<chunksize=(31, 723, 491), meta=np.ndarray>
- short_name :
- SG_N
- units :
- g N m-2
- long_name :
- Seagrass N
- _ChunkSizes :
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Array Chunk Bytes 44.02 MB 44.02 MB Shape (31, 723, 491) (31, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - SG_N_pr(time, latitude, longitude)float32dask.array<chunksize=(31, 723, 491), meta=np.ndarray>
- short_name :
- SG_N_pr
- units :
- mg N m-2 d-1
- long_name :
- Seagrass net production
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Array Chunk Bytes 44.02 MB 44.02 MB Shape (31, 723, 491) (31, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - SG_shear_mort(time, latitude, longitude)float32dask.array<chunksize=(31, 723, 491), meta=np.ndarray>
- short_name :
- SG_shear_mort
- units :
- g N m-2 d-1
- long_name :
- Seagrass shear stress mort
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Array Chunk Bytes 44.02 MB 44.02 MB Shape (31, 723, 491) (31, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - SGD_N(time, latitude, longitude)float32dask.array<chunksize=(31, 723, 491), meta=np.ndarray>
- short_name :
- SGD_N
- units :
- g N m-2
- long_name :
- Deep seagrass N
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Array Chunk Bytes 44.02 MB 44.02 MB Shape (31, 723, 491) (31, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - SGD_N_pr(time, latitude, longitude)float32dask.array<chunksize=(31, 723, 491), meta=np.ndarray>
- short_name :
- SGD_N_pr
- units :
- mg N m-2 d-1
- long_name :
- Deep seagrass net production
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Array Chunk Bytes 44.02 MB 44.02 MB Shape (31, 723, 491) (31, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - SGD_shear_mort(time, latitude, longitude)float32dask.array<chunksize=(31, 723, 491), meta=np.ndarray>
- short_name :
- SGD_shear_mort
- units :
- g N m-2 d-1
- long_name :
- Deep seagrass shear stress mort
- _ChunkSizes :
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Array Chunk Bytes 44.02 MB 44.02 MB Shape (31, 723, 491) (31, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - SGH_N(time, latitude, longitude)float32dask.array<chunksize=(31, 723, 491), meta=np.ndarray>
- short_name :
- SGH_N
- units :
- g N m-2
- long_name :
- Halophila N
- _ChunkSizes :
- [ 1 133 491]
Array Chunk Bytes 44.02 MB 44.02 MB Shape (31, 723, 491) (31, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - SGH_N_pr(time, latitude, longitude)float32dask.array<chunksize=(31, 723, 491), meta=np.ndarray>
- short_name :
- SGH_N_pr
- units :
- mg N m-2 d-1
- long_name :
- Halophila net production
- _ChunkSizes :
- [ 1 133 491]
Array Chunk Bytes 44.02 MB 44.02 MB Shape (31, 723, 491) (31, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - SGHROOT_N(time, latitude, longitude)float32dask.array<chunksize=(31, 723, 491), meta=np.ndarray>
- short_name :
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- units :
- g N m-2
- long_name :
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- _ChunkSizes :
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Array Chunk Bytes 44.02 MB 44.02 MB Shape (31, 723, 491) (31, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - SGROOT_N(time, latitude, longitude)float32dask.array<chunksize=(31, 723, 491), meta=np.ndarray>
- short_name :
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- units :
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- long_name :
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- _ChunkSizes :
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Array Chunk Bytes 44.02 MB 44.02 MB Shape (31, 723, 491) (31, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - TSSM(time, latitude, longitude)float32dask.array<chunksize=(31, 723, 491), meta=np.ndarray>
- short_name :
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- units :
- g TSS m-3
- long_name :
- TSS from 645 nm (Petus et al., 2014)
- _ChunkSizes :
- [ 1 133 491]
Array Chunk Bytes 44.02 MB 44.02 MB Shape (31, 723, 491) (31, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - Zenith2D(time, latitude, longitude)float32dask.array<chunksize=(31, 723, 491), meta=np.ndarray>
- short_name :
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- units :
- rad
- long_name :
- Solar zenith
- _ChunkSizes :
- [ 1 133 491]
Array Chunk Bytes 44.02 MB 44.02 MB Shape (31, 723, 491) (31, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray
- Conventions :
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- NCO :
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- RunID :
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- _CoordSysBuilder :
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- aims_ncaggregate_buildDate :
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- aims_ncaggregate_datasetId :
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- aims_ncaggregate_firstDate :
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- aims_ncaggregate_inputs :
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- aims_ncaggregate_lastDate :
- 2018-01-31T12:00:00+10:00
- codehead :
- CSIRO Environmental Modelling Suite
- description :
- Regridding of daily input data (from eReefs AIMS-CSIRO GBR4 BioGeoChemical 3.1 subset) from curvilinear (per input data) to rectilinear via inverse weighted distance from up to 4 closest cells.
- ems_version :
- v1.1.1 rev(6244M)
- history :
- Tue Oct 8 15:38:27 2019: ncatted -a positive,botz,o,char,up -a missing_value,botz,o,double,99. -a outside,botz,o,double,-9999. gbr4_bgc_all_simple_2018-01.nc 2020-08-20T23:45:30+10:00: vendor: AIMS; processing: None summaries 2020-08-21T23:07:30+10:00: vendor: AIMS; processing: None summaries
- metadata_link :
- https://eatlas.org.au/data/uuid/61f3a6df-2c4a-46b6-ab62-3f3a9bf4e87a
- paramfile :
- /home/bai155/EMS_solar2/gbr4_H2p0_B3p1_Cb/tran/GBR4_H2p0_B3p1_Cq3b_Dhnd.tran
- paramhead :
- eReefs 4 km grid. SOURCE Catchments with 2019 condition from Dec 1, 2010 to June,30, 2018, Empirical SOURCE with 2019 condition, Jul 1, 2018 to April 30, 2019. More details of naming protocol at: eReefs.info.
- technical_guide_link :
- https://eatlas.org.au/pydio/public/aims-ereefs-platform-technical-guide-to-derived-products-from-csiro-ereefs-models-pdf
- technical_guide_publish_date :
- 2020-08-18
- title :
- eReefs AIMS-CSIRO GBR4 BioGeoChemical 3.1 (baseline catchment conditions) daily aggregation
- DODS_EXTRA.Unlimited_Dimension :
- time
base_url2 = "http://thredds.ereefs.aims.gov.au/thredds/dodsC/s3://aims-ereefs-public-prod/derived/ncaggregate/ereefs/gbr4_v2/daily-monthly/EREEFS_AIMS-CSIRO_gbr4_v2_hydro_daily-monthly-"
hydrofiles = [f"{base_url2}{year}-{month:02}.nc" for month in range(1, 2)]
hydrofiles
['http://thredds.ereefs.aims.gov.au/thredds/dodsC/s3://aims-ereefs-public-prod/derived/ncaggregate/ereefs/gbr4_v2/daily-monthly/EREEFS_AIMS-CSIRO_gbr4_v2_hydro_daily-monthly-2018-01.nc']
ds_hydro = xr.open_mfdataset(hydrofiles)
ds_hydro
<xarray.Dataset>
Dimensions: (k: 17, latitude: 723, longitude: 491, time: 31)
Coordinates:
zc (k) float64 dask.array<chunksize=(17,), meta=np.ndarray>
* time (time) datetime64[ns] 2017-12-31T14:00:00 ... 2018-01-30T14:...
* latitude (latitude) float64 -28.7 -28.67 -28.64 ... -7.096 -7.066 -7.036
* longitude (longitude) float64 142.2 142.2 142.2 ... 156.8 156.8 156.9
Dimensions without coordinates: k
Data variables:
mean_cur (time, k, latitude, longitude) float32 dask.array<chunksize=(31, 17, 723, 491), meta=np.ndarray>
salt (time, k, latitude, longitude) float32 dask.array<chunksize=(31, 17, 723, 491), meta=np.ndarray>
temp (time, k, latitude, longitude) float32 dask.array<chunksize=(31, 17, 723, 491), meta=np.ndarray>
u (time, k, latitude, longitude) float32 dask.array<chunksize=(31, 17, 723, 491), meta=np.ndarray>
v (time, k, latitude, longitude) float32 dask.array<chunksize=(31, 17, 723, 491), meta=np.ndarray>
mean_wspeed (time, latitude, longitude) float32 dask.array<chunksize=(31, 723, 491), meta=np.ndarray>
eta (time, latitude, longitude) float32 dask.array<chunksize=(31, 723, 491), meta=np.ndarray>
wspeed_u (time, latitude, longitude) float32 dask.array<chunksize=(31, 723, 491), meta=np.ndarray>
wspeed_v (time, latitude, longitude) float32 dask.array<chunksize=(31, 723, 491), meta=np.ndarray>
Attributes: (12/19)
Conventions: CF-1.0
Run_ID: 2
_CoordSysBuilder: ucar.nc2.dataset.conv.CF1Convention
aims_ncaggregate_buildDate: 2020-08-21T12:50:07+10:00
aims_ncaggregate_datasetId: products__ncaggregate__ereefs__gbr4_v2__...
aims_ncaggregate_firstDate: 2018-01-01T00:00:00+10:00
... ...
paramhead: GBR 4km resolution grid
shoc_version: v1.1 rev(5620)
technical_guide_link: https://eatlas.org.au/pydio/public/aims-...
technical_guide_publish_date: 2020-08-18
title: eReefs AIMS-CSIRO GBR4 Hydrodynamic v2 d...
DODS_EXTRA.Unlimited_Dimension: timexarray.Dataset
- k: 17
- latitude: 723
- longitude: 491
- time: 31
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- positive :
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- units :
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- axis :
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- _CoordinateAxisType :
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- _CoordinateZisPositive :
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- long_name :
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- standard_name :
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- coordinate_type :
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- _ChunkSizes :
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array(['2017-12-31T14:00:00.000000000', '2018-01-01T14:00:00.000000000', '2018-01-02T14:00:00.000000000', '2018-01-03T14:00:00.000000000', '2018-01-04T14:00:00.000000000', '2018-01-05T14:00:00.000000000', '2018-01-06T14:00:00.000000000', '2018-01-07T14:00:00.000000000', '2018-01-08T14:00:00.000000000', '2018-01-09T14:00:00.000000000', '2018-01-10T14:00:00.000000000', '2018-01-11T14:00:00.000000000', '2018-01-12T14:00:00.000000000', '2018-01-13T14:00:00.000000000', '2018-01-14T14:00:00.000000000', '2018-01-15T14:00:00.000000000', '2018-01-16T14:00:00.000000000', '2018-01-17T14:00:00.000000000', '2018-01-18T14:00:00.000000000', '2018-01-19T14:00:00.000000000', '2018-01-20T14:00:00.000000000', '2018-01-21T14:00:00.000000000', '2018-01-22T14:00:00.000000000', '2018-01-23T14:00:00.000000000', '2018-01-24T14:00:00.000000000', '2018-01-25T14:00:00.000000000', '2018-01-26T14:00:00.000000000', '2018-01-27T14:00:00.000000000', '2018-01-28T14:00:00.000000000', '2018-01-29T14:00:00.000000000', '2018-01-30T14:00:00.000000000'], dtype='datetime64[ns]') - latitude(latitude)float64-28.7 -28.67 ... -7.066 -7.036
- long_name :
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- standard_name :
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- units :
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- coordinate_type :
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- _CoordinateAxisType :
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array([-28.696022, -28.666022, -28.636022, ..., -7.096022, -7.066022, -7.036022]) - longitude(longitude)float64142.2 142.2 142.2 ... 156.8 156.9
- standard_name :
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- long_name :
- Longitude
- units :
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- coordinate_type :
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- projection :
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- _CoordinateAxisType :
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array([142.168788, 142.198788, 142.228788, ..., 156.808788, 156.838788, 156.868788])
- mean_cur(time, k, latitude, longitude)float32dask.array<chunksize=(31, 17, 723, 491), meta=np.ndarray>
- substanceOrTaxon_id :
- http://environment.data.gov.au/def/feature/ocean_current
- units :
- ms-1
- medium_id :
- http://environment.data.gov.au/def/feature/ocean
- unit_id :
- http://qudt.org/vocab/unit#MeterPerSecond
- short_name :
- mean_cur
- aggregation :
- mean_speed
- standard_name :
- mean_current_speed
- long_name :
- mean_current_speed
- _ChunkSizes :
- [ 1 1 133 491]
Array Chunk Bytes 748.33 MB 748.33 MB Shape (31, 17, 723, 491) (31, 17, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - salt(time, k, latitude, longitude)float32dask.array<chunksize=(31, 17, 723, 491), meta=np.ndarray>
- substanceOrTaxon_id :
- http://sweet.jpl.nasa.gov/2.2/matrWater.owl#SaltWater
- scaledQuantityKind_id :
- http://environment.data.gov.au/def/property/practical_salinity
- short_name :
- salt
- aggregation :
- Daily
- units :
- PSU
- medium_id :
- http://environment.data.gov.au/def/feature/ocean
- unit_id :
- http://environment.data.gov.au/water/quality/def/unit/PSU
- long_name :
- Salinity
- _ChunkSizes :
- [ 1 1 133 491]
Array Chunk Bytes 748.33 MB 748.33 MB Shape (31, 17, 723, 491) (31, 17, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - temp(time, k, latitude, longitude)float32dask.array<chunksize=(31, 17, 723, 491), meta=np.ndarray>
- substanceOrTaxon_id :
- http://sweet.jpl.nasa.gov/2.2/matrWater.owl#SaltWater
- scaledQuantityKind_id :
- http://environment.data.gov.au/def/property/sea_water_temperature
- short_name :
- temp
- aggregation :
- Daily
- units :
- degrees C
- medium_id :
- http://environment.data.gov.au/def/feature/ocean
- unit_id :
- http://qudt.org/vocab/unit#DegreeCelsius
- long_name :
- Temperature
- _ChunkSizes :
- [ 1 1 133 491]
Array Chunk Bytes 748.33 MB 748.33 MB Shape (31, 17, 723, 491) (31, 17, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - u(time, k, latitude, longitude)float32dask.array<chunksize=(31, 17, 723, 491), meta=np.ndarray>
- vector_components :
- u v
- substanceOrTaxon_id :
- http://environment.data.gov.au/def/feature/ocean_current
- scaledQuantityKind_id :
- http://environment.data.gov.au/def/property/sea_water_velocity_eastward
- short_name :
- u
- vector_name :
- Currents
- standard_name :
- eastward_sea_water_velocity
- aggregation :
- Daily
- units :
- ms-1
- medium_id :
- http://environment.data.gov.au/def/feature/ocean
- unit_id :
- http://qudt.org/vocab/unit#MeterPerSecond
- long_name :
- Eastward current
- _ChunkSizes :
- [ 1 1 133 491]
Array Chunk Bytes 748.33 MB 748.33 MB Shape (31, 17, 723, 491) (31, 17, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - v(time, k, latitude, longitude)float32dask.array<chunksize=(31, 17, 723, 491), meta=np.ndarray>
- vector_components :
- u v
- substanceOrTaxon_id :
- http://environment.data.gov.au/def/feature/ocean_current
- scaledQuantityKind_id :
- http://environment.data.gov.au/def/property/sea_water_velocity_northward
- short_name :
- v
- vector_name :
- Currents
- standard_name :
- northward_sea_water_velocity
- aggregation :
- Daily
- units :
- ms-1
- medium_id :
- http://environment.data.gov.au/def/feature/ocean
- unit_id :
- http://qudt.org/vocab/unit#MeterPerSecond
- long_name :
- Northward current
- _ChunkSizes :
- [ 1 1 133 491]
Array Chunk Bytes 748.33 MB 748.33 MB Shape (31, 17, 723, 491) (31, 17, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - mean_wspeed(time, latitude, longitude)float32dask.array<chunksize=(31, 723, 491), meta=np.ndarray>
- units :
- ms-1
- short_name :
- mean_wspeed
- aggregation :
- mean_speed
- standard_name :
- mean_wind_speed
- long_name :
- mean_wind_speed
- _ChunkSizes :
- [ 1 133 491]
Array Chunk Bytes 44.02 MB 44.02 MB Shape (31, 723, 491) (31, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - eta(time, latitude, longitude)float32dask.array<chunksize=(31, 723, 491), meta=np.ndarray>
- substanceOrTaxon_id :
- http://environment.data.gov.au/def/feature/ocean_near_surface
- scaledQuantityKind_id :
- http://environment.data.gov.au/def/property/sea_surface_elevation
- short_name :
- eta
- standard_name :
- sea_surface_height_above_sea_level
- aggregation :
- Daily
- units :
- metre
- positive :
- up
- medium_id :
- http://environment.data.gov.au/def/feature/ocean
- unit_id :
- http://qudt.org/vocab/unit#Meter
- long_name :
- Surface elevation
- _ChunkSizes :
- [ 1 133 491]
Array Chunk Bytes 44.02 MB 44.02 MB Shape (31, 723, 491) (31, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - wspeed_u(time, latitude, longitude)float32dask.array<chunksize=(31, 723, 491), meta=np.ndarray>
- short_name :
- wspeed_u
- aggregation :
- Daily
- units :
- ms-1
- long_name :
- eastward_wind
- _ChunkSizes :
- [ 1 133 491]
Array Chunk Bytes 44.02 MB 44.02 MB Shape (31, 723, 491) (31, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - wspeed_v(time, latitude, longitude)float32dask.array<chunksize=(31, 723, 491), meta=np.ndarray>
- short_name :
- wspeed_v
- aggregation :
- Daily
- units :
- ms-1
- long_name :
- northward_wind
- _ChunkSizes :
- [ 1 133 491]
Array Chunk Bytes 44.02 MB 44.02 MB Shape (31, 723, 491) (31, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray
- Conventions :
- CF-1.0
- Run_ID :
- 2
- _CoordSysBuilder :
- ucar.nc2.dataset.conv.CF1Convention
- aims_ncaggregate_buildDate :
- 2020-08-21T12:50:07+10:00
- aims_ncaggregate_datasetId :
- products__ncaggregate__ereefs__gbr4_v2__daily-monthly/EREEFS_AIMS-CSIRO_gbr4_v2_hydro_daily-monthly-2018-01
- aims_ncaggregate_firstDate :
- 2018-01-01T00:00:00+10:00
- aims_ncaggregate_inputs :
- [products__ncaggregate__ereefs__gbr4_v2__raw/EREEFS_AIMS-CSIRO_gbr4_v2_hydro_raw_2018-01::MD5:f3d285e0aaa945e64825e628387ac6d0]
- aims_ncaggregate_lastDate :
- 2018-01-31T00:00:00+10:00
- description :
- Aggregation of raw hourly input data (from eReefs AIMS-CSIRO GBR4 Hydrodynamic v2 subset) to daily means. Also calculates mean magnitude of wind and ocean current speeds. Data is regridded from curvilinear (per input data) to rectilinear via inverse weighted distance from up to 4 closest cells.
- hasVocab :
- 1
- history :
- 2020-08-20T12:56:06+10:00: vendor: AIMS; processing: None summaries 2020-08-21T12:50:07+10:00: vendor: AIMS; processing: Daily summaries
- metadata_link :
- https://eatlas.org.au/data/uuid/350aed53-ae0f-436e-9866-d34db7f04d2e
- paramfile :
- in.prm
- paramhead :
- GBR 4km resolution grid
- shoc_version :
- v1.1 rev(5620)
- technical_guide_link :
- https://eatlas.org.au/pydio/public/aims-ereefs-platform-technical-guide-to-derived-products-from-csiro-ereefs-models-pdf
- technical_guide_publish_date :
- 2020-08-18
- title :
- eReefs AIMS-CSIRO GBR4 Hydrodynamic v2 daily aggregation
- DODS_EXTRA.Unlimited_Dimension :
- time
reef_lat = -18.82
reef_lon = 147.64
min_lon = 146
min_lat = -21
max_lon = 150
max_lat = -16
lon_bnds = [min_lon, max_lon]
lat_bnds = [min_lat, max_lat]
ds_bio_clip = ds_bio.sel(latitude=slice(*lat_bnds), longitude=slice(*lon_bnds))
ds_hydro_clip = ds_hydro.sel(latitude=slice(*lat_bnds), longitude=slice(*lon_bnds))
zcvar = -1
new_ds = ds_hydro_clip.isel(k=zcvar)
new_ds
<xarray.Dataset>
Dimensions: (latitude: 167, longitude: 134, time: 31)
Coordinates:
zc float64 dask.array<chunksize=(), meta=np.ndarray>
* time (time) datetime64[ns] 2017-12-31T14:00:00 ... 2018-01-30T14:...
* latitude (latitude) float64 -20.99 -20.96 -20.93 ... -16.04 -16.01
* longitude (longitude) float64 146.0 146.0 146.1 ... 149.9 150.0 150.0
Data variables:
mean_cur (time, latitude, longitude) float32 dask.array<chunksize=(31, 167, 134), meta=np.ndarray>
salt (time, latitude, longitude) float32 dask.array<chunksize=(31, 167, 134), meta=np.ndarray>
temp (time, latitude, longitude) float32 dask.array<chunksize=(31, 167, 134), meta=np.ndarray>
u (time, latitude, longitude) float32 dask.array<chunksize=(31, 167, 134), meta=np.ndarray>
v (time, latitude, longitude) float32 dask.array<chunksize=(31, 167, 134), meta=np.ndarray>
mean_wspeed (time, latitude, longitude) float32 dask.array<chunksize=(31, 167, 134), meta=np.ndarray>
eta (time, latitude, longitude) float32 dask.array<chunksize=(31, 167, 134), meta=np.ndarray>
wspeed_u (time, latitude, longitude) float32 dask.array<chunksize=(31, 167, 134), meta=np.ndarray>
wspeed_v (time, latitude, longitude) float32 dask.array<chunksize=(31, 167, 134), meta=np.ndarray>
Attributes: (12/19)
Conventions: CF-1.0
Run_ID: 2
_CoordSysBuilder: ucar.nc2.dataset.conv.CF1Convention
aims_ncaggregate_buildDate: 2020-08-21T12:50:07+10:00
aims_ncaggregate_datasetId: products__ncaggregate__ereefs__gbr4_v2__...
aims_ncaggregate_firstDate: 2018-01-01T00:00:00+10:00
... ...
paramhead: GBR 4km resolution grid
shoc_version: v1.1 rev(5620)
technical_guide_link: https://eatlas.org.au/pydio/public/aims-...
technical_guide_publish_date: 2020-08-18
title: eReefs AIMS-CSIRO GBR4 Hydrodynamic v2 d...
DODS_EXTRA.Unlimited_Dimension: timexarray.Dataset
- latitude: 167
- longitude: 134
- time: 31
- zc()float64dask.array<chunksize=(), meta=np.ndarray>
- positive :
- up
- coordinate_type :
- Z
- units :
- m
- long_name :
- Z coordinate
- axis :
- Z
- _CoordinateAxisType :
- Height
- _CoordinateZisPositive :
- up
Array Chunk Bytes 8 B 8 B Shape () () Count 3 Tasks 1 Chunks Type float64 numpy.ndarray - time(time)datetime64[ns]2017-12-31T14:00:00 ... 2018-01-...
- long_name :
- Time
- standard_name :
- time
- coordinate_type :
- time
- _CoordinateAxisType :
- Time
- _ChunkSizes :
- 1024
array(['2017-12-31T14:00:00.000000000', '2018-01-01T14:00:00.000000000', '2018-01-02T14:00:00.000000000', '2018-01-03T14:00:00.000000000', '2018-01-04T14:00:00.000000000', '2018-01-05T14:00:00.000000000', '2018-01-06T14:00:00.000000000', '2018-01-07T14:00:00.000000000', '2018-01-08T14:00:00.000000000', '2018-01-09T14:00:00.000000000', '2018-01-10T14:00:00.000000000', '2018-01-11T14:00:00.000000000', '2018-01-12T14:00:00.000000000', '2018-01-13T14:00:00.000000000', '2018-01-14T14:00:00.000000000', '2018-01-15T14:00:00.000000000', '2018-01-16T14:00:00.000000000', '2018-01-17T14:00:00.000000000', '2018-01-18T14:00:00.000000000', '2018-01-19T14:00:00.000000000', '2018-01-20T14:00:00.000000000', '2018-01-21T14:00:00.000000000', '2018-01-22T14:00:00.000000000', '2018-01-23T14:00:00.000000000', '2018-01-24T14:00:00.000000000', '2018-01-25T14:00:00.000000000', '2018-01-26T14:00:00.000000000', '2018-01-27T14:00:00.000000000', '2018-01-28T14:00:00.000000000', '2018-01-29T14:00:00.000000000', '2018-01-30T14:00:00.000000000'], dtype='datetime64[ns]') - latitude(latitude)float64-20.99 -20.96 ... -16.04 -16.01
- long_name :
- Latitude
- standard_name :
- latitude
- units :
- degrees_north
- coordinate_type :
- latitude
- projection :
- geographic
- _CoordinateAxisType :
- Lat
array([-20.986022, -20.956022, -20.926022, -20.896022, -20.866022, -20.836022, -20.806022, -20.776022, -20.746022, -20.716022, -20.686022, -20.656022, -20.626022, -20.596022, -20.566022, -20.536022, -20.506022, -20.476022, -20.446022, -20.416022, -20.386022, -20.356022, -20.326022, -20.296022, -20.266022, -20.236022, -20.206022, -20.176022, -20.146022, -20.116022, -20.086022, -20.056022, -20.026022, -19.996022, -19.966022, -19.936022, -19.906022, -19.876022, -19.846022, -19.816022, -19.786022, -19.756022, -19.726022, -19.696022, -19.666022, -19.636022, -19.606022, -19.576022, -19.546022, -19.516022, -19.486022, -19.456022, -19.426022, -19.396022, -19.366022, -19.336022, -19.306022, -19.276022, -19.246022, -19.216022, -19.186022, -19.156022, -19.126022, -19.096022, -19.066022, -19.036022, -19.006022, -18.976022, -18.946022, -18.916022, -18.886022, -18.856022, -18.826022, -18.796022, -18.766022, -18.736022, -18.706022, -18.676022, -18.646022, -18.616022, -18.586022, -18.556022, -18.526022, -18.496022, -18.466022, -18.436022, -18.406022, -18.376022, -18.346022, -18.316022, -18.286022, -18.256022, -18.226022, -18.196022, -18.166022, -18.136022, -18.106022, -18.076022, -18.046022, -18.016022, -17.986022, -17.956022, -17.926022, -17.896022, -17.866022, -17.836022, -17.806022, -17.776022, -17.746022, -17.716022, -17.686022, -17.656022, -17.626022, -17.596022, -17.566022, -17.536022, -17.506022, -17.476022, -17.446022, -17.416022, -17.386022, -17.356022, -17.326022, -17.296022, -17.266022, -17.236022, -17.206022, -17.176022, -17.146022, -17.116022, -17.086022, -17.056022, -17.026022, -16.996022, -16.966022, -16.936022, -16.906022, -16.876022, -16.846022, -16.816022, -16.786022, -16.756022, -16.726022, -16.696022, -16.666022, -16.636022, -16.606022, -16.576022, -16.546022, -16.516022, -16.486022, -16.456022, -16.426022, -16.396022, -16.366022, -16.336022, -16.306022, -16.276022, -16.246022, -16.216022, -16.186022, -16.156022, -16.126022, -16.096022, -16.066022, -16.036022, -16.006022]) - longitude(longitude)float64146.0 146.0 146.1 ... 150.0 150.0
- standard_name :
- longitude
- long_name :
- Longitude
- units :
- degrees_east
- coordinate_type :
- longitude
- projection :
- geographic
- _CoordinateAxisType :
- Lon
array([146.008788, 146.038788, 146.068788, 146.098788, 146.128788, 146.158788, 146.188788, 146.218788, 146.248788, 146.278788, 146.308788, 146.338788, 146.368788, 146.398788, 146.428788, 146.458788, 146.488788, 146.518788, 146.548788, 146.578788, 146.608788, 146.638788, 146.668788, 146.698788, 146.728788, 146.758788, 146.788788, 146.818788, 146.848788, 146.878788, 146.908788, 146.938788, 146.968788, 146.998788, 147.028788, 147.058788, 147.088788, 147.118788, 147.148788, 147.178788, 147.208788, 147.238788, 147.268788, 147.298788, 147.328788, 147.358788, 147.388788, 147.418788, 147.448788, 147.478788, 147.508788, 147.538788, 147.568788, 147.598788, 147.628788, 147.658788, 147.688788, 147.718788, 147.748788, 147.778788, 147.808788, 147.838788, 147.868788, 147.898788, 147.928788, 147.958788, 147.988788, 148.018788, 148.048788, 148.078788, 148.108788, 148.138788, 148.168788, 148.198788, 148.228788, 148.258788, 148.288788, 148.318788, 148.348788, 148.378788, 148.408788, 148.438788, 148.468788, 148.498788, 148.528788, 148.558788, 148.588788, 148.618788, 148.648788, 148.678788, 148.708788, 148.738788, 148.768788, 148.798788, 148.828788, 148.858788, 148.888788, 148.918788, 148.948788, 148.978788, 149.008788, 149.038788, 149.068788, 149.098788, 149.128788, 149.158788, 149.188788, 149.218788, 149.248788, 149.278788, 149.308788, 149.338788, 149.368788, 149.398788, 149.428788, 149.458788, 149.488788, 149.518788, 149.548788, 149.578788, 149.608788, 149.638788, 149.668788, 149.698788, 149.728788, 149.758788, 149.788788, 149.818788, 149.848788, 149.878788, 149.908788, 149.938788, 149.968788, 149.998788])
- mean_cur(time, latitude, longitude)float32dask.array<chunksize=(31, 167, 134), meta=np.ndarray>
- substanceOrTaxon_id :
- http://environment.data.gov.au/def/feature/ocean_current
- units :
- ms-1
- medium_id :
- http://environment.data.gov.au/def/feature/ocean
- unit_id :
- http://qudt.org/vocab/unit#MeterPerSecond
- short_name :
- mean_cur
- aggregation :
- mean_speed
- standard_name :
- mean_current_speed
- long_name :
- mean_current_speed
- _ChunkSizes :
- [ 1 1 133 491]
Array Chunk Bytes 2.77 MB 2.77 MB Shape (31, 167, 134) (31, 167, 134) Count 4 Tasks 1 Chunks Type float32 numpy.ndarray - salt(time, latitude, longitude)float32dask.array<chunksize=(31, 167, 134), meta=np.ndarray>
- substanceOrTaxon_id :
- http://sweet.jpl.nasa.gov/2.2/matrWater.owl#SaltWater
- scaledQuantityKind_id :
- http://environment.data.gov.au/def/property/practical_salinity
- short_name :
- salt
- aggregation :
- Daily
- units :
- PSU
- medium_id :
- http://environment.data.gov.au/def/feature/ocean
- unit_id :
- http://environment.data.gov.au/water/quality/def/unit/PSU
- long_name :
- Salinity
- _ChunkSizes :
- [ 1 1 133 491]
Array Chunk Bytes 2.77 MB 2.77 MB Shape (31, 167, 134) (31, 167, 134) Count 4 Tasks 1 Chunks Type float32 numpy.ndarray - temp(time, latitude, longitude)float32dask.array<chunksize=(31, 167, 134), meta=np.ndarray>
- substanceOrTaxon_id :
- http://sweet.jpl.nasa.gov/2.2/matrWater.owl#SaltWater
- scaledQuantityKind_id :
- http://environment.data.gov.au/def/property/sea_water_temperature
- short_name :
- temp
- aggregation :
- Daily
- units :
- degrees C
- medium_id :
- http://environment.data.gov.au/def/feature/ocean
- unit_id :
- http://qudt.org/vocab/unit#DegreeCelsius
- long_name :
- Temperature
- _ChunkSizes :
- [ 1 1 133 491]
Array Chunk Bytes 2.77 MB 2.77 MB Shape (31, 167, 134) (31, 167, 134) Count 4 Tasks 1 Chunks Type float32 numpy.ndarray - u(time, latitude, longitude)float32dask.array<chunksize=(31, 167, 134), meta=np.ndarray>
- vector_components :
- u v
- substanceOrTaxon_id :
- http://environment.data.gov.au/def/feature/ocean_current
- scaledQuantityKind_id :
- http://environment.data.gov.au/def/property/sea_water_velocity_eastward
- short_name :
- u
- vector_name :
- Currents
- standard_name :
- eastward_sea_water_velocity
- aggregation :
- Daily
- units :
- ms-1
- medium_id :
- http://environment.data.gov.au/def/feature/ocean
- unit_id :
- http://qudt.org/vocab/unit#MeterPerSecond
- long_name :
- Eastward current
- _ChunkSizes :
- [ 1 1 133 491]
Array Chunk Bytes 2.77 MB 2.77 MB Shape (31, 167, 134) (31, 167, 134) Count 4 Tasks 1 Chunks Type float32 numpy.ndarray - v(time, latitude, longitude)float32dask.array<chunksize=(31, 167, 134), meta=np.ndarray>
- vector_components :
- u v
- substanceOrTaxon_id :
- http://environment.data.gov.au/def/feature/ocean_current
- scaledQuantityKind_id :
- http://environment.data.gov.au/def/property/sea_water_velocity_northward
- short_name :
- v
- vector_name :
- Currents
- standard_name :
- northward_sea_water_velocity
- aggregation :
- Daily
- units :
- ms-1
- medium_id :
- http://environment.data.gov.au/def/feature/ocean
- unit_id :
- http://qudt.org/vocab/unit#MeterPerSecond
- long_name :
- Northward current
- _ChunkSizes :
- [ 1 1 133 491]
Array Chunk Bytes 2.77 MB 2.77 MB Shape (31, 167, 134) (31, 167, 134) Count 4 Tasks 1 Chunks Type float32 numpy.ndarray - mean_wspeed(time, latitude, longitude)float32dask.array<chunksize=(31, 167, 134), meta=np.ndarray>
- units :
- ms-1
- short_name :
- mean_wspeed
- aggregation :
- mean_speed
- standard_name :
- mean_wind_speed
- long_name :
- mean_wind_speed
- _ChunkSizes :
- [ 1 133 491]
Array Chunk Bytes 2.77 MB 2.77 MB Shape (31, 167, 134) (31, 167, 134) Count 3 Tasks 1 Chunks Type float32 numpy.ndarray - eta(time, latitude, longitude)float32dask.array<chunksize=(31, 167, 134), meta=np.ndarray>
- substanceOrTaxon_id :
- http://environment.data.gov.au/def/feature/ocean_near_surface
- scaledQuantityKind_id :
- http://environment.data.gov.au/def/property/sea_surface_elevation
- short_name :
- eta
- standard_name :
- sea_surface_height_above_sea_level
- aggregation :
- Daily
- units :
- metre
- positive :
- up
- medium_id :
- http://environment.data.gov.au/def/feature/ocean
- unit_id :
- http://qudt.org/vocab/unit#Meter
- long_name :
- Surface elevation
- _ChunkSizes :
- [ 1 133 491]
Array Chunk Bytes 2.77 MB 2.77 MB Shape (31, 167, 134) (31, 167, 134) Count 3 Tasks 1 Chunks Type float32 numpy.ndarray - wspeed_u(time, latitude, longitude)float32dask.array<chunksize=(31, 167, 134), meta=np.ndarray>
- short_name :
- wspeed_u
- aggregation :
- Daily
- units :
- ms-1
- long_name :
- eastward_wind
- _ChunkSizes :
- [ 1 133 491]
Array Chunk Bytes 2.77 MB 2.77 MB Shape (31, 167, 134) (31, 167, 134) Count 3 Tasks 1 Chunks Type float32 numpy.ndarray - wspeed_v(time, latitude, longitude)float32dask.array<chunksize=(31, 167, 134), meta=np.ndarray>
- short_name :
- wspeed_v
- aggregation :
- Daily
- units :
- ms-1
- long_name :
- northward_wind
- _ChunkSizes :
- [ 1 133 491]
Array Chunk Bytes 2.77 MB 2.77 MB Shape (31, 167, 134) (31, 167, 134) Count 3 Tasks 1 Chunks Type float32 numpy.ndarray
- Conventions :
- CF-1.0
- Run_ID :
- 2
- _CoordSysBuilder :
- ucar.nc2.dataset.conv.CF1Convention
- aims_ncaggregate_buildDate :
- 2020-08-21T12:50:07+10:00
- aims_ncaggregate_datasetId :
- products__ncaggregate__ereefs__gbr4_v2__daily-monthly/EREEFS_AIMS-CSIRO_gbr4_v2_hydro_daily-monthly-2018-01
- aims_ncaggregate_firstDate :
- 2018-01-01T00:00:00+10:00
- aims_ncaggregate_inputs :
- [products__ncaggregate__ereefs__gbr4_v2__raw/EREEFS_AIMS-CSIRO_gbr4_v2_hydro_raw_2018-01::MD5:f3d285e0aaa945e64825e628387ac6d0]
- aims_ncaggregate_lastDate :
- 2018-01-31T00:00:00+10:00
- description :
- Aggregation of raw hourly input data (from eReefs AIMS-CSIRO GBR4 Hydrodynamic v2 subset) to daily means. Also calculates mean magnitude of wind and ocean current speeds. Data is regridded from curvilinear (per input data) to rectilinear via inverse weighted distance from up to 4 closest cells.
- hasVocab :
- 1
- history :
- 2020-08-20T12:56:06+10:00: vendor: AIMS; processing: None summaries 2020-08-21T12:50:07+10:00: vendor: AIMS; processing: Daily summaries
- metadata_link :
- https://eatlas.org.au/data/uuid/350aed53-ae0f-436e-9866-d34db7f04d2e
- paramfile :
- in.prm
- paramhead :
- GBR 4km resolution grid
- shoc_version :
- v1.1 rev(5620)
- technical_guide_link :
- https://eatlas.org.au/pydio/public/aims-ereefs-platform-technical-guide-to-derived-products-from-csiro-ereefs-models-pdf
- technical_guide_publish_date :
- 2020-08-18
- title :
- eReefs AIMS-CSIRO GBR4 Hydrodynamic v2 daily aggregation
- DODS_EXTRA.Unlimited_Dimension :
- time
week_ds = new_ds.resample(time='1W').mean(dim='time').drop('zc')[['mean_cur','u','v']]
week_ds
<xarray.Dataset>
Dimensions: (latitude: 167, longitude: 134, time: 6)
Coordinates:
* time (time) datetime64[ns] 2017-12-31 2018-01-07 ... 2018-02-04
* latitude (latitude) float64 -20.99 -20.96 -20.93 ... -16.07 -16.04 -16.01
* longitude (longitude) float64 146.0 146.0 146.1 146.1 ... 149.9 150.0 150.0
Data variables:
mean_cur (time, latitude, longitude) float32 dask.array<chunksize=(1, 167, 134), meta=np.ndarray>
u (time, latitude, longitude) float32 dask.array<chunksize=(1, 167, 134), meta=np.ndarray>
v (time, latitude, longitude) float32 dask.array<chunksize=(1, 167, 134), meta=np.ndarray>xarray.Dataset
- latitude: 167
- longitude: 134
- time: 6
- time(time)datetime64[ns]2017-12-31 ... 2018-02-04
array(['2017-12-31T00:00:00.000000000', '2018-01-07T00:00:00.000000000', '2018-01-14T00:00:00.000000000', '2018-01-21T00:00:00.000000000', '2018-01-28T00:00:00.000000000', '2018-02-04T00:00:00.000000000'], dtype='datetime64[ns]') - latitude(latitude)float64-20.99 -20.96 ... -16.04 -16.01
- long_name :
- Latitude
- standard_name :
- latitude
- units :
- degrees_north
- coordinate_type :
- latitude
- projection :
- geographic
- _CoordinateAxisType :
- Lat
array([-20.986022, -20.956022, -20.926022, -20.896022, -20.866022, -20.836022, -20.806022, -20.776022, -20.746022, -20.716022, -20.686022, -20.656022, -20.626022, -20.596022, -20.566022, -20.536022, -20.506022, -20.476022, -20.446022, -20.416022, -20.386022, -20.356022, -20.326022, -20.296022, -20.266022, -20.236022, -20.206022, -20.176022, -20.146022, -20.116022, -20.086022, -20.056022, -20.026022, -19.996022, -19.966022, -19.936022, -19.906022, -19.876022, -19.846022, -19.816022, -19.786022, -19.756022, -19.726022, -19.696022, -19.666022, -19.636022, -19.606022, -19.576022, -19.546022, -19.516022, -19.486022, -19.456022, -19.426022, -19.396022, -19.366022, -19.336022, -19.306022, -19.276022, -19.246022, -19.216022, -19.186022, -19.156022, -19.126022, -19.096022, -19.066022, -19.036022, -19.006022, -18.976022, -18.946022, -18.916022, -18.886022, -18.856022, -18.826022, -18.796022, -18.766022, -18.736022, -18.706022, -18.676022, -18.646022, -18.616022, -18.586022, -18.556022, -18.526022, -18.496022, -18.466022, -18.436022, -18.406022, -18.376022, -18.346022, -18.316022, -18.286022, -18.256022, -18.226022, -18.196022, -18.166022, -18.136022, -18.106022, -18.076022, -18.046022, -18.016022, -17.986022, -17.956022, -17.926022, -17.896022, -17.866022, -17.836022, -17.806022, -17.776022, -17.746022, -17.716022, -17.686022, -17.656022, -17.626022, -17.596022, -17.566022, -17.536022, -17.506022, -17.476022, -17.446022, -17.416022, -17.386022, -17.356022, -17.326022, -17.296022, -17.266022, -17.236022, -17.206022, -17.176022, -17.146022, -17.116022, -17.086022, -17.056022, -17.026022, -16.996022, -16.966022, -16.936022, -16.906022, -16.876022, -16.846022, -16.816022, -16.786022, -16.756022, -16.726022, -16.696022, -16.666022, -16.636022, -16.606022, -16.576022, -16.546022, -16.516022, -16.486022, -16.456022, -16.426022, -16.396022, -16.366022, -16.336022, -16.306022, -16.276022, -16.246022, -16.216022, -16.186022, -16.156022, -16.126022, -16.096022, -16.066022, -16.036022, -16.006022]) - longitude(longitude)float64146.0 146.0 146.1 ... 150.0 150.0
- standard_name :
- longitude
- long_name :
- Longitude
- units :
- degrees_east
- coordinate_type :
- longitude
- projection :
- geographic
- _CoordinateAxisType :
- Lon
array([146.008788, 146.038788, 146.068788, 146.098788, 146.128788, 146.158788, 146.188788, 146.218788, 146.248788, 146.278788, 146.308788, 146.338788, 146.368788, 146.398788, 146.428788, 146.458788, 146.488788, 146.518788, 146.548788, 146.578788, 146.608788, 146.638788, 146.668788, 146.698788, 146.728788, 146.758788, 146.788788, 146.818788, 146.848788, 146.878788, 146.908788, 146.938788, 146.968788, 146.998788, 147.028788, 147.058788, 147.088788, 147.118788, 147.148788, 147.178788, 147.208788, 147.238788, 147.268788, 147.298788, 147.328788, 147.358788, 147.388788, 147.418788, 147.448788, 147.478788, 147.508788, 147.538788, 147.568788, 147.598788, 147.628788, 147.658788, 147.688788, 147.718788, 147.748788, 147.778788, 147.808788, 147.838788, 147.868788, 147.898788, 147.928788, 147.958788, 147.988788, 148.018788, 148.048788, 148.078788, 148.108788, 148.138788, 148.168788, 148.198788, 148.228788, 148.258788, 148.288788, 148.318788, 148.348788, 148.378788, 148.408788, 148.438788, 148.468788, 148.498788, 148.528788, 148.558788, 148.588788, 148.618788, 148.648788, 148.678788, 148.708788, 148.738788, 148.768788, 148.798788, 148.828788, 148.858788, 148.888788, 148.918788, 148.948788, 148.978788, 149.008788, 149.038788, 149.068788, 149.098788, 149.128788, 149.158788, 149.188788, 149.218788, 149.248788, 149.278788, 149.308788, 149.338788, 149.368788, 149.398788, 149.428788, 149.458788, 149.488788, 149.518788, 149.548788, 149.578788, 149.608788, 149.638788, 149.668788, 149.698788, 149.728788, 149.758788, 149.788788, 149.818788, 149.848788, 149.878788, 149.908788, 149.938788, 149.968788, 149.998788])
- mean_cur(time, latitude, longitude)float32dask.array<chunksize=(1, 167, 134), meta=np.ndarray>
Array Chunk Bytes 537.07 kB 89.51 kB Shape (6, 167, 134) (1, 167, 134) Count 34 Tasks 6 Chunks Type float32 numpy.ndarray - u(time, latitude, longitude)float32dask.array<chunksize=(1, 167, 134), meta=np.ndarray>
Array Chunk Bytes 537.07 kB 89.51 kB Shape (6, 167, 134) (1, 167, 134) Count 34 Tasks 6 Chunks Type float32 numpy.ndarray - v(time, latitude, longitude)float32dask.array<chunksize=(1, 167, 134), meta=np.ndarray>
Array Chunk Bytes 537.07 kB 89.51 kB Shape (6, 167, 134) (1, 167, 134) Count 34 Tasks 6 Chunks Type float32 numpy.ndarray
load_week = week_ds.load()
load_week
---------------------------------------------------------------------------
KeyboardInterrupt Traceback (most recent call last)
<ipython-input-11-9de0457538f4> in <module>
1 load_week = week_ds.load()
----> 2 load_week
/usr/share/miniconda/envs/envireef/lib/python3.8/site-packages/IPython/core/displayhook.py in __call__(self, result)
260 self.start_displayhook()
261 self.write_output_prompt()
--> 262 format_dict, md_dict = self.compute_format_data(result)
263 self.update_user_ns(result)
264 self.fill_exec_result(result)
/usr/share/miniconda/envs/envireef/lib/python3.8/site-packages/IPython/core/displayhook.py in compute_format_data(self, result)
149
150 """
--> 151 return self.shell.display_formatter.format(result)
152
153 # This can be set to True by the write_output_prompt method in a subclass
/usr/share/miniconda/envs/envireef/lib/python3.8/site-packages/IPython/core/formatters.py in format(self, obj, include, exclude)
178 md = None
179 try:
--> 180 data = formatter(obj)
181 except:
182 # FIXME: log the exception
<decorator-gen-2> in __call__(self, obj)
/usr/share/miniconda/envs/envireef/lib/python3.8/site-packages/IPython/core/formatters.py in catch_format_error(method, self, *args, **kwargs)
222 """show traceback on failed format call"""
223 try:
--> 224 r = method(self, *args, **kwargs)
225 except NotImplementedError:
226 # don't warn on NotImplementedErrors
/usr/share/miniconda/envs/envireef/lib/python3.8/site-packages/IPython/core/formatters.py in __call__(self, obj)
343 method = get_real_method(obj, self.print_method)
344 if method is not None:
--> 345 return method()
346 return None
347 else:
/usr/share/miniconda/envs/envireef/lib/python3.8/site-packages/xarray/core/dataset.py in _repr_html_(self)
1808 if OPTIONS["display_style"] == "text":
1809 return f"<pre>{escape(repr(self))}</pre>"
-> 1810 return formatting_html.dataset_repr(self)
1811
1812 def info(self, buf=None) -> None:
/usr/share/miniconda/envs/envireef/lib/python3.8/site-packages/xarray/core/formatting_html.py in dataset_repr(ds)
281 sections = [
282 dim_section(ds),
--> 283 coord_section(ds.coords),
284 datavar_section(ds.data_vars),
285 attr_section(ds.attrs),
/usr/share/miniconda/envs/envireef/lib/python3.8/site-packages/xarray/core/formatting_html.py in _mapping_section(mapping, name, details_func, max_items_collapse, enabled)
171 return collapsible_section(
172 name,
--> 173 details=details_func(mapping),
174 n_items=n_items,
175 enabled=enabled,
/usr/share/miniconda/envs/envireef/lib/python3.8/site-packages/xarray/core/formatting_html.py in summarize_coords(variables)
90 coords = {}
91 for k, v in variables.items():
---> 92 coords.update(**summarize_coord(k, v))
93
94 vars_li = "".join(f"<li class='xr-var-item'>{v}</li>" for v in coords.values())
/usr/share/miniconda/envs/envireef/lib/python3.8/site-packages/xarray/core/formatting_html.py in summarize_coord(name, var)
84 return coords
85
---> 86 return {name: summarize_variable(name, var, is_index)}
87
88
/usr/share/miniconda/envs/envireef/lib/python3.8/site-packages/xarray/core/formatting_html.py in summarize_variable(name, var, is_index, dtype, preview)
112 preview = preview or escape(inline_variable_array_repr(variable, 35))
113 attrs_ul = summarize_attrs(var.attrs)
--> 114 data_repr = short_data_repr_html(variable)
115
116 attrs_icon = _icon("icon-file-text2")
/usr/share/miniconda/envs/envireef/lib/python3.8/site-packages/xarray/core/formatting_html.py in short_data_repr_html(array)
26 return internal_data._repr_html_()
27 else:
---> 28 text = escape(short_data_repr(array))
29 return f"<pre>{text}</pre>"
30
/usr/share/miniconda/envs/envireef/lib/python3.8/site-packages/xarray/core/formatting.py in short_data_repr(array)
481 return limit_lines(repr(array.data), limit=40)
482 elif array._in_memory or array.size < 1e5:
--> 483 return short_numpy_repr(array)
484 else:
485 # internal xarray array type
/usr/share/miniconda/envs/envireef/lib/python3.8/site-packages/xarray/core/formatting.py in short_numpy_repr(array)
470 options["edgeitems"] = edgeitems
471 with set_numpy_options(**options):
--> 472 return repr(array)
473
474
/usr/share/miniconda/envs/envireef/lib/python3.8/site-packages/numpy/core/arrayprint.py in _array_repr_implementation(arr, max_line_width, precision, suppress_small, array2string)
1411 lst = repr(arr.item())
1412 elif arr.size > 0 or arr.shape == (0,):
-> 1413 lst = array2string(arr, max_line_width, precision, suppress_small,
1414 ', ', prefix, suffix=suffix)
1415 else: # show zero-length shape unless it is (0,)
/usr/share/miniconda/envs/envireef/lib/python3.8/site-packages/numpy/core/arrayprint.py in array2string(a, max_line_width, precision, suppress_small, separator, prefix, style, formatter, threshold, edgeitems, sign, floatmode, suffix, legacy)
690 return "[]"
691
--> 692 return _array2string(a, options, separator, prefix)
693
694
/usr/share/miniconda/envs/envireef/lib/python3.8/site-packages/numpy/core/arrayprint.py in wrapper(self, *args, **kwargs)
466 repr_running.add(key)
467 try:
--> 468 return f(self, *args, **kwargs)
469 finally:
470 repr_running.discard(key)
/usr/share/miniconda/envs/envireef/lib/python3.8/site-packages/numpy/core/arrayprint.py in _array2string(a, options, separator, prefix)
499 next_line_prefix += " "*len(prefix)
500
--> 501 lst = _formatArray(a, format_function, options['linewidth'],
502 next_line_prefix, separator, options['edgeitems'],
503 summary_insert, options['legacy'])
/usr/share/miniconda/envs/envireef/lib/python3.8/site-packages/numpy/core/arrayprint.py in _formatArray(a, format_function, line_width, next_line_prefix, separator, edge_items, summary_insert, legacy)
843 try:
844 # invoke the recursive part with an initial index and prefix
--> 845 return recurser(index=(),
846 hanging_indent=next_line_prefix,
847 curr_width=line_width)
/usr/share/miniconda/envs/envireef/lib/python3.8/site-packages/numpy/core/arrayprint.py in recurser(index, hanging_indent, curr_width)
799
800 for i in range(trailing_items, 1, -1):
--> 801 word = recurser(index + (-i,), next_hanging_indent, next_width)
802 s, line = _extendLine_pretty(
803 s, line, word, elem_width, hanging_indent, legacy)
/usr/share/miniconda/envs/envireef/lib/python3.8/site-packages/numpy/core/arrayprint.py in recurser(index, hanging_indent, curr_width)
753
754 if axes_left == 0:
--> 755 return format_function(a[index])
756
757 # when recursing, add a space to align with the [ added, and reduce the
/usr/share/miniconda/envs/envireef/lib/python3.8/site-packages/numpy/core/arrayprint.py in __call__(self, x)
992 exp_digits=self.exp_size)
993 else:
--> 994 return dragon4_positional(x,
995 precision=self.precision,
996 unique=self.unique,
KeyboardInterrupt:
lat = load_week.latitude
lon = load_week.longitude
# Convert to magnitude and angle
mag = np.sqrt(load_week.u**2 + load_week.v**2)
angle = (np.pi/2.) - np.arctan2(load_week.u/mag, load_week.v/mag)
load_week["mag"] = (['time', 'latitude', 'longitude'], mag)
load_week["angle"] = (['time', 'latitude', 'longitude'], angle)
dataset = gv.Dataset(load_week, ['longitude', 'latitude', 'time'],
'mag', crs=crs.PlateCarree())
images = dataset.to(gv.Image,dynamic=True)
coastline = gf.coastline(line_width=2,line_color='k').opts(projection=crs.PlateCarree(),scale='10m')
land = gf.land.options(scale='10m', fill_color='lightgray')
resample = load_week.isel(longitude=slice(None, None, 7), latitude=slice(None, None, 7))
resample
<xarray.Dataset>
Dimensions: (latitude: 24, longitude: 20, time: 6)
Coordinates:
* time (time) datetime64[ns] 2017-12-31 2018-01-07 ... 2018-02-04
* latitude (latitude) float64 -20.99 -20.78 -20.57 ... -16.58 -16.37 -16.16
* longitude (longitude) float64 146.0 146.2 146.4 146.6 ... 149.6 149.8 150.0
Data variables:
mean_cur (time, latitude, longitude) float32 nan nan nan ... 0.2521 0.1418
u (time, latitude, longitude) float32 nan nan ... -0.0868 -0.0706
v (time, latitude, longitude) float32 nan nan ... -0.07677 0.004869
mag (time, latitude, longitude) float32 nan nan ... 0.1159 0.07077
angle (time, latitude, longitude) float32 nan nan nan ... 3.866 3.073xarray.Dataset
- latitude: 24
- longitude: 20
- time: 6
- time(time)datetime64[ns]2017-12-31 ... 2018-02-04
array(['2017-12-31T00:00:00.000000000', '2018-01-07T00:00:00.000000000', '2018-01-14T00:00:00.000000000', '2018-01-21T00:00:00.000000000', '2018-01-28T00:00:00.000000000', '2018-02-04T00:00:00.000000000'], dtype='datetime64[ns]') - latitude(latitude)float64-20.99 -20.78 ... -16.37 -16.16
- long_name :
- Latitude
- standard_name :
- latitude
- units :
- degrees_north
- coordinate_type :
- latitude
- projection :
- geographic
- _CoordinateAxisType :
- Lat
array([-20.986022, -20.776022, -20.566022, -20.356022, -20.146022, -19.936022, -19.726022, -19.516022, -19.306022, -19.096022, -18.886022, -18.676022, -18.466022, -18.256022, -18.046022, -17.836022, -17.626022, -17.416022, -17.206022, -16.996022, -16.786022, -16.576022, -16.366022, -16.156022]) - longitude(longitude)float64146.0 146.2 146.4 ... 149.8 150.0
- standard_name :
- longitude
- long_name :
- Longitude
- units :
- degrees_east
- coordinate_type :
- longitude
- projection :
- geographic
- _CoordinateAxisType :
- Lon
array([146.008788, 146.218788, 146.428788, 146.638788, 146.848788, 147.058788, 147.268788, 147.478788, 147.688788, 147.898788, 148.108788, 148.318788, 148.528788, 148.738788, 148.948788, 149.158788, 149.368788, 149.578788, 149.788788, 149.998788])
- mean_cur(time, latitude, longitude)float32nan nan nan ... 0.2521 0.1418
array([[[ nan, nan, nan, ..., 0.47212517, 0.5389019 , 0.49233517], [ nan, nan, nan, ..., 0.7100594 , 0.5719123 , 0.58482116], [ nan, nan, nan, ..., 0.52848905, 0.5838511 , 0.74972516], ..., [0.34381664, 0.679885 , 0.86086744, ..., 0.13400985, 0.16307776, 0.18038848], [0.47148544, 0.9085537 , 0.6430302 , ..., 0.18541881, 0.25834343, 0.22849731], [0.7261729 , 0.6786929 , 0.5035176 , ..., 0.23139587, 0.27227044, 0.17955069]], [[ nan, nan, nan, ..., 0.3911107 , 0.43052644, 0.34461585], [ nan, nan, nan, ..., 0.5830327 , 0.41155967, 0.40362623], [ nan, nan, nan, ..., 0.3689634 , 0.41570213, 0.62644094], ... [0.2184902 , 0.22487698, 0.14303575, ..., 0.37622228, 0.29722866, 0.34020042], [0.22258158, 0.14548217, 0.1967626 , ..., 0.33652082, 0.33747774, 0.3314679 ], [0.17729208, 0.19564995, 0.23313804, ..., 0.33100682, 0.35367486, 0.30175972]], [[ nan, nan, nan, ..., 0.48992074, 0.54045796, 0.42153725], [ nan, nan, nan, ..., 0.7172849 , 0.5158851 , 0.48960897], [ nan, nan, nan, ..., 0.45912445, 0.51580644, 0.7737384 ], ..., [0.199566 , 0.1488688 , 0.1708673 , ..., 0.22341418, 0.22770667, 0.23896709], [0.1770459 , 0.14235942, 0.10880828, ..., 0.24432036, 0.27054036, 0.22990195], [0.13048047, 0.11833389, 0.16496147, ..., 0.27626908, 0.25208396, 0.14183167]]], dtype=float32) - u(time, latitude, longitude)float32nan nan nan ... -0.0868 -0.0706
array([[[ nan, nan, nan, ..., 6.90529048e-02, 1.03673868e-01, 1.72459379e-01], [ nan, nan, nan, ..., 4.51745912e-02, 2.34789506e-01, 3.03944796e-01], [ nan, nan, nan, ..., 2.80943185e-01, 2.91898638e-01, 4.70331907e-02], ..., [ 2.02928424e-01, 3.57508600e-01, 4.84615088e-01, ..., -2.50112847e-03, 5.72354533e-03, -2.01321784e-02], [ 1.30542502e-01, 3.80064934e-01, 3.19767118e-01, ..., -4.41572592e-02, -1.91314779e-02, 1.45560387e-03], [ 2.00617343e-01, 2.09721655e-01, 1.66954949e-01, ..., 2.26108842e-02, 3.89686525e-02, 4.16807421e-02]], [[ nan, nan, nan, ..., -2.19765864e-02, 8.27628560e-03, 1.78846717e-02], [ nan, nan, nan, ..., 3.09401454e-04, 1.19394260e-02, 1.13033934e-03], [ nan, nan, nan, ..., 1.47448601e-02, 3.11529892e-03, 1.92552041e-02], ... -2.24685177e-01, -1.49603948e-01, -1.92662969e-01], [-1.40438244e-01, -9.63505954e-02, -1.44292563e-01, ..., -1.59651071e-01, -1.36651263e-01, -1.50970265e-01], [-1.27297163e-01, -1.48783371e-01, -1.89100578e-01, ..., -1.25761330e-01, -1.92826718e-01, -1.68952063e-01]], [[ nan, nan, nan, ..., -3.99443470e-02, 2.02701427e-04, 1.45507837e-02], [ nan, nan, nan, ..., 3.48759373e-03, 1.01210456e-02, -1.63850337e-02], [ nan, nan, nan, ..., 8.32328945e-03, -5.73209301e-03, 2.80068479e-02], ..., [ 4.09470797e-02, 4.62391004e-02, 1.25270709e-02, ..., -1.58079952e-01, -1.16193712e-01, -1.51177168e-01], [-4.50067827e-03, -1.22496970e-02, -4.14707214e-02, ..., -3.58810201e-02, -7.91790038e-02, -1.26932204e-01], [-4.92708534e-02, -6.14223629e-02, -6.16034567e-02, ..., -5.48696704e-02, -8.67971703e-02, -7.05993176e-02]]], dtype=float32) - v(time, latitude, longitude)float32nan nan nan ... -0.07677 0.004869
array([[[ nan, nan, nan, ..., -9.22872201e-02, -1.12038709e-01, -2.18105197e-01], [ nan, nan, nan, ..., -1.51910737e-01, -2.00827986e-01, -1.78377613e-01], [ nan, nan, nan, ..., -1.64801076e-01, -1.71736479e-01, -1.37968764e-01], ..., [-1.97084591e-01, -5.66674232e-01, -7.03773260e-01, ..., -4.83269058e-02, -7.57153332e-02, -2.35245284e-02], [-4.30329889e-01, -8.22518289e-01, -5.55430412e-01, ..., -7.71712735e-02, -1.21884786e-01, -3.46619301e-02], [-6.94736540e-01, -6.42317116e-01, -4.70725685e-01, ..., -6.49455935e-02, -8.24693665e-02, -9.51747745e-02]], [[ nan, nan, nan, ..., -1.28579959e-02, -3.10299229e-02, -7.87110105e-02], [ nan, nan, nan, ..., -4.62445281e-02, -8.20691809e-02, -9.07100886e-02], [ nan, nan, nan, ..., -8.17326158e-02, -9.36146304e-02, -3.59886698e-02], ... -2.04296261e-01, -1.27199396e-01, -1.57235503e-01], [ 1.41055420e-01, 3.13900970e-02, 5.42678162e-02, ..., -2.20029399e-01, -1.54622793e-01, -1.64385423e-01], [ 6.30570576e-02, 8.76969174e-02, 8.92044306e-02, ..., -1.86016425e-01, -1.52943745e-01, -7.75674582e-02]], [[ nan, nan, nan, ..., -1.13591328e-02, -2.41903830e-02, -2.55565066e-02], [ nan, nan, nan, ..., -8.65738541e-02, -3.84498760e-02, -1.69890784e-02], [ nan, nan, nan, ..., -5.27146459e-02, -3.01908404e-02, -4.48103324e-02], ..., [-6.56357184e-02, -5.64542934e-02, -1.21519648e-01, ..., -9.19738859e-02, -2.36685462e-02, -4.19514254e-02], [-8.63877609e-02, -8.52285549e-02, -5.27223945e-03, ..., -9.58645567e-02, -4.31749448e-02, -3.64411399e-02], [-4.44015637e-02, 2.69967765e-02, 3.99933085e-02, ..., -3.46073769e-02, -7.67708048e-02, 4.86919656e-03]]], dtype=float32) - mag(time, latitude, longitude)float32nan nan nan ... 0.1159 0.07077
array([[[ nan, nan, nan, ..., 0.11526159, 0.15264647, 0.27805054], [ nan, nan, nan, ..., 0.15848538, 0.30896276, 0.35242164], [ nan, nan, nan, ..., 0.32571223, 0.33867127, 0.14576523], ..., [0.2828821 , 0.670024 , 0.8544873 , ..., 0.04839158, 0.07593136, 0.03096301], [0.4496945 , 0.9060826 , 0.6409009 , ..., 0.08891158, 0.12337712, 0.03469248], [0.72312254, 0.67568815, 0.49945632, ..., 0.06876905, 0.09121267, 0.10390151]], [[ nan, nan, nan, ..., 0.02546171, 0.03211468, 0.08071731], [ nan, nan, nan, ..., 0.04624556, 0.08293311, 0.09071714], [ nan, nan, nan, ..., 0.08305198, 0.09366645, 0.04081602], ... [0.18545608, 0.19729038, 0.09178084, ..., 0.30367813, 0.19636962, 0.24868056], [0.19904655, 0.10133497, 0.15416011, ..., 0.2718481 , 0.20635352, 0.22319183], [0.142059 , 0.17270565, 0.20908482, ..., 0.22453958, 0.24611773, 0.18590726]], [[ nan, nan, nan, ..., 0.04152807, 0.02419123, 0.02940851], [ nan, nan, nan, ..., 0.08664408, 0.03975964, 0.02360293], [ nan, nan, nan, ..., 0.0533677 , 0.03073018, 0.05284269], ..., [0.07736091, 0.07297356, 0.12216363, ..., 0.18288921, 0.11857983, 0.15688996], [0.08650492, 0.08610436, 0.04180451, ..., 0.10235947, 0.09018531, 0.1320596 ], [0.06632584, 0.06709346, 0.07344692, ..., 0.06487181, 0.11587711, 0.07076703]]], dtype=float32) - angle(time, latitude, longitude)float32nan nan nan ... 3.704 3.866 3.073
array([[[ nan, nan, nan, ..., -0.92842305, -0.8241564 , -0.9017416 ], [ nan, nan, nan, ..., -1.2817487 , -0.7075938 , -0.5307127 ], [ nan, nan, nan, ..., -0.5305077 , -0.53180397, -1.2422537 ], ..., [-0.77079 , -1.0079733 , -0.96776545, ..., 4.660681 , -1.4953469 , 4.00454 ], [-1.2762648 , -1.1379465 , -1.0484282 , ..., 4.1926627 , 4.556696 , -1.5288266 ], [-1.2896761 , -1.255201 , -1.2299622 , ..., -1.2357694 , -1.129371 , -1.1580175 ]], [[ nan, nan, nan, ..., 3.670967 , -1.3101445 , -1.3473703 ], [ nan, nan, nan, ..., -1.5641059 , -1.4263297 , -1.5583359 ], [ nan, nan, nan, ..., -1.3923124 , -1.5375305 , -1.0795149 ], ... [ 2.315703 , 2.428811 , 3.642921 , ..., 3.879498 , 3.8462267 , 3.8260841 ], [ 2.354002 , 2.826646 , 2.7818618 , ..., 4.0846944 , 3.9886127 , 3.9695048 ], [ 2.6816695 , 2.608984 , 2.7008157 , ..., 4.1179004 , 3.8121533 , 3.5719962 ]], [[ nan, nan, nan, ..., 3.4186528 , -1.5624171 , -1.053213 ], [ nan, nan, nan, ..., -1.5305334 , -1.313408 , 3.945088 ], [ nan, nan, nan, ..., -1.4141959 , 4.5247602 , -1.0121907 ], ..., [-1.0130218 , -0.88454473, -1.4680723 , ..., 3.6685362 , 3.3425426 , 3.41228 ], [ 4.6603374 , 4.569639 , 3.268046 , ..., 4.3542423 , 3.6408072 , 3.421165 ], [ 3.8750553 , 2.7274823 , 2.565776 , ..., 3.7042942 , 3.8657699 , 3.0727324 ]]], dtype=float32)
resample.mag.max()
<xarray.DataArray 'mag' ()> array(0.98175, dtype=float32)
xarray.DataArray
'mag'
- 0.9818
array(0.98175, dtype=float32)
lat = resample.latitude
lon = resample.longitude
# label = 'Arrows scale with plot width, not view'
# vectorfield = hv.VectorField((lon, lat, resample.angle[0,:,:], resample.mag[0,:,:]))
# vectorfield.relabel(label)
# kdims=[hv.Dimension('step', values=np.arange(0,6)),
# hv.Dimension('time', values=resample.time.values)]
# hmap = hv.HoloMap(vfield, kdims=kdims)
#hmap = hv.DynamicMap(vfield, kdims=kdims)
max_mag = resample.mag.max()
nb = resample.angle.shape[0]
a_var = resample.time.values
y_list = []
for i in range(nb):
y_list.append(gv.VectorField((lon, lat, resample.angle[i,:,:], resample.mag[i,:,:]/max_mag), crs=crs.PlateCarree()))
# create HoloMap object
dict_y = {a_var[i]:y_list[i] for i in range(nb)}
hmap = hv.HoloMap(dict_y, kdims="time").opts(opts.VectorField(magnitude=dim('Magnitude')*0.25, color='k', width=600, height=500,
pivot='tip', line_width=1, title='eReefs GBR 4km',
rescale_lengths=False,
projection=crs.PlateCarree()))
# hmap
images.opts(cmap=cmocean.cm.speed, colorbar=True, width=500, height=500, clim=(0.1,1.0)) * coastline * land * hmap